The increasing complexity of the electromagnetic environment poses significant challenges to jamming countermeasures. Active jamming recognition serves as a crucial prerequisite for active jamming countermeasures. Addressing the blindness of feature extraction in radar active jamming recognition, this paper proposes an active jamming recognition method based on Fisher discriminant and feature selection. This method employs the Fisher discriminant criterion to theoretically derive feature separability and rank the importance of feature attributes. the features with the best performance are selected as inputs for the classifier. The proposed method substantially reduces the computational cost of feature extraction without compromising recognition accuracy. In simulation experiments, when jamming-to-noise ratio is above$-$5 dB, the recognition rate consistently exceeds 96%, demonstrating superior generalization and robustness.

In the hyperspectral image (HSI) classification, convolutional neural networks (CNNs)-based approaches often struggle with the scarcity of labeled samples. The letter proposes an HSI classification method based on data expansion and consistency regularization with small samples. Specifically, we leverage the pixel-pair feature (PPF) to expand the dataset, which facilitates the adequate tuning of CNN parameters and alleviates the issue of overfitting. In addition, a designed CNN structure is employed to extract discriminative features from the limited number of labeled PPFs and numerous unlabeled PPFs. The CNN is trained via minimizing the weighted sum of supervised and unsupervised losses, where the supervised loss is calculated through the cross-entropy function while the unsupervised loss is evaluated with the consistency regularization item. Moreover, reliable references required in the consistency regularization item are provided after making an exponential moving average (EMA) on the outputs of CNNs at different training epochs. Ultimately, we conduct experiments on three real HSI datasets, and the results show that the proposed approach gains superior classification accuracy compared to several existing CNN-based approaches.

Synthetic aperture radar (SAR) automatic target recognition (ATR) has been widely used by scholars around the world and achieved excellent results. However, occluded SAR target recognition is still a very challenging task. In this letter, we propose a center local constraint shadow residual network (ClcsrNet) for occluded SAR target recognition. First, the shadow features of SAR images are extracted to improve the robustness of the network to occlusion. Then, the shadow features, the target convolutional features, and the residual features are fused to increase the feature diversity of the network. Finally, we combine the center loss and the local constraint loss to optimize the network. The center loss is used to better cluster the targets in the same class. The local constraint loss is used to maintain the local structure of the target, which increases the separability between different classes. Experiments on the moving and stationary target acquisition and recognition (MSTAR) datasets demonstrate that the proposed ClcsrNet can achieve higher accuracy and better robustness than the comparison algorithms in occluded SAR target recognition.

For high-resolution squinted airborne synthetic aperture radar (SAR) imaging, both linear range walk correction (LRWC) and motion error introduce significant azimuth spatial-variant (ASV) characteristics in the radar echo, rendering the classical assumption of "azimuth translational invariance" no longer valid. Existing sub-aperture methods attempt to overcome the ASV characteristics of the signal by performing segmentation processing in the data domain or the image domain. However, grating lobes or image stitching problems inevitably occur in the focused images. Existing full-aperture methods, on the other hand, utilize azimuth resampling or nonlinear chirp scaling (NCS) to address the ASV problem. Nevertheless, the above-mentioned methods basically handle the ASV characteristics introduced by LRWC and motion errors separately, without considering the coupling characteristics between the two. Therefore, this paper proposes a high phase-preservation squint airborne SAR autofocus imaging method by modifying the traditional azimuth resampling processing, so that only a single azimuth resampling factor is required to simultaneously solve the ASV problems brought about by LRWC and motion errors. The imaging processing results of airborne squint SAR real-data verify its good focusing effect. Meanwhile, the interferometric processing results of multi-pass cross-track SAR real-data also indicate that the proposed algorithm exhibits a high phase-preservation capacity. The images processed by the proposed algorithm and the comparison algorithms, as well as the multi-pass cross-track SAR complex images after registration, can be downloaded from https://pan.baidu.com/s/1okgAkp18ynK7qzKXe2lceQ?pwd=nquf.

As a novel configuration of along-track Multistatic SAR (Multi-SAR), the high frame rate Along-Track Swarm SAR (ATS-SAR) has garnered significant attention in recent years due to its exceptional efficiency in reducing data acquisition time. Motivated by its potential for high-resolution imaging of moving targets, this paper investigates the application of ATS-SAR in moving target imaging. However, high frame rate ATS-SAR-based moving target imaging confronts two critical challenges: time-space coupling and partial data loss in moving target echoes. To address these challenges, we first conduct a comprehensive analysis and theoretical derivation of the moving target echo model under the high frame rate ATS-SAR configuration. Subsequently, we propose an innovative motion parameter estimation algorithm that exploits unique echo characteristics to achieve high-performance imaging. Furthermore, we introduce the highresolution, high frame rate ATS-SAR Sub-Aperture Collaborative Imaging algorithm for Moving Targets (MT-SACIm-ATS). Extensive simulations and a real measured experiment validate the effectiveness of the MT-SACIm-ATS algorithm, demonstrating imaging performance that closely approximates reference imaging results. Comparative analysis with several state-of-the-art algorithms further highlights the superiority of the proposed approach in terms of resolution and robustness.

Polar Format Algorithm (PFA) is effective for single-channel high-resolution synthetic aperture radar (SAR) imaging. However, to image for high-resolution and wide swath (HRWS) SAR, the traditional methods for multichannel (MC) SAR require an extra signal reconstruction process. When the signal reconstruction is combined with the PFA, some advantages of PFA, such as simple implementation and high efficiency, cannot be retained. In this paper, a PFA for MC SAR (MC-PFA) is proposed which avoids the extra signal reconstruction by a frequency-band reweighting interpolation (FBRI) proposed in this paper, thus retaining the simplicity and high efficiency of the traditional PFA. In the MC-PFA, the FBRI is combined with the azimuth interpolation in the traditional PFA. Compared to the azimuth interpolation of the traditional PFA, the combined processing can fulfill the range cell migration correction and the signal reconstruction simultaneously without additional interpolation. Furthermore, when the MC-PFA is combined with the Generalized PFA (GPFA), it can be further extended to MC sliding spotlight SAR and MC Terrain Observation by Progressive Scans (TOPS) SAR. Simulation experiments verify the effectiveness of the algorithm proposed in this paper.

Robust registration of multimodal remote sensing images (MRSIs) remains a challenge due to nonlinear intensity differences (NIDs) and geometric distortions. Existing methods typically rely on maximum index maps (MIMs) to construct feature descriptors. However, MIM is sensitive to rotation variations, under which circumstance the number of correct matches decreases under severe geometric distortions. To address these problems, this study proposes an ingenious registration method (named as CSFD-AFM) based on a cyclic shift feature descriptor (CSFD) and an adaptive feature matching (AFM). The CSFD is a rotation-invariant descriptor that enables efficient orientation adjustment through cyclic shifts of a three-dimensional feature array constructed from a modified MIM. To achieve rotation invariance, a dominant-orientation-based method for cyclic shift is introduced. Based on the CSFD, we devise a matching strategy named AFM, which adaptively adjusts the scale and orientation of CSFDs for robust and accurate feature matching. Experiments were conducted on six MRSI datasets to evaluate the performance of the proposed CSFD-AFM. The results suggest that the proposed CSFD-AFM outperforms the existing seven state-of-the-art methods including ReDFeat, MINIMA-LG, RIFT2, MS-HLMO, HOWP, POS-GIFT, and GLS-MIFT. In addition, our proposed CSFD-AFM achieves the accuracy within 2 pixels while maintaining its robustness against severe geometric distortions such as large rotations.

With the advancement of Microwave Photonic (MWP) synthetic aperture radar (SAR) technology, resolution has increased to 0.02 m, and platforms have expanded from airborne to vehicle-borne. Incorporating ultra-wideband, long synthetic aperture, and varied observation ranges presents two primary challenges for MWP SAR imaging: 1) The enhancement of two-dimensional (2-D) resolution renders the imaging process more susceptible to 2-D space-variant motion errors (SVMEs). 2) The expansion of application platforms, particularly close-range observation by vehicle-borne platforms, invalidates traditional imaging algorithms based on the far-field assumption. To address the challenges, a novel time-domain processing framework is proposed for both airborne and vehicle-borne MWP SAR systems. Firstly, we analyzes wavenumber spectrum resampling during the back-projection (BP) process, establishing a mapping relationship between phase errors in image and time domain. This allows for the estimation of trajectory deviation, enabling a rough estimation of the 2-D SVME. Subsequently, a motion compensation (MoCo) method, based on an overlapping sub-image configuration combined with fast ground Cartesian BPA (GCBPA), is introduced to enable imaging. This method solves the problem that MoCo method cannot be integrated with fast GCBPA. In the third stage, the relationship between azimuth phase error (APE) and 2-D phase error is established. Leveraging this relationship, a 2-D wavenumber domain autofocus method is developed to concurrently compensate for APE and nonsystematic range cell migration (NsRCM). Experimental validations on both airborne (0.03m) and vehicle-borne (0.02m) MWP SAR platforms data confirm the effectiveness and versatility of the proposed time-domain processing framework.

In recent years, the ground Cartesian back-projection (GCBP) algorithm has demonstrated significant advantages for bistatic forward-looking synthetic aperture radar (BFSAR) imaging with arbitrary geometries and complex configurations, primarily due to its interpolation-free operation. However, airborne BFSAR systems must additionally address motion error compensation challenges. Implementing effective motion compensation (MoCo) within the GCBP framework for BFSAR presents two key challenges: 1) The forward-looking configuration induces severe image spectrum tilt and nonsystematic range cell migration (NsRCM), significantly degrading phase error estimation accuracy; and 2) Spectrum resampling during BP processing obstructs direct time domain phase error (TDPE) estimation from the image. To address these challenges, this paper proposes a phase error reverse recovery method based on ground combined beam coordinate (GCBC) for BFSAR. The proposed MoCo method establishes the GCBC system aligned with the echo signal’s azimuth Doppler variation, which eliminates image spectrum tilt and reduces NsRCM caused by the bistatic configuration. Within this system, we introduce the spectrum center correction and spectrum tilt correction, which effectively remove image spectrum aliasing and enable accurate image domain phase error (IDPE) estimation. Furthermore, an analytical reverse recovery relationship between IDPE and TDPE is derived. These stages significantly enhance the accuracy and robustness of BFSAR motion error estimation. Simulation and real data results demonstrate the proposed method’s superior performance.

By equipping the synthetic aperture radar (SAR) sensors on multiple unmanned aerial vehicles (UAVs) to form a UAV swarm (UAVS) and operate collaboratively, UAVS-SAR presents the significant advantages of rapid echoes acquisition, high imaging frame rate as well as high system survivability for advanced SAR applications. However, due to the flexible trajectories as well as the distributed configuration, the problem of the spectrum blurring in the UAVS-SAR is more complicated than that of the conventional monostatic/bistatic SAR configurations, which makes the current fast time domain algorithms (FTDAs) difficult to achieve high imaging performance. In this paper, a novel fast time domain algorithm (FTDA) is developed for UAVS-SAR imaging with both high efficiency and promising accuracy. By developing the hierarchical framework based on the designed spectrum alignment function, the imaging procedures can be realized recursively where back projection (BP) operations are reduced dramatically, and then, the total computational burden are decreased consequently. Moreover, the trajectory difference of the UAVS formation is particularly considered for practical applications, which will inevitably degrade the coherence among the sub-images from different UAV platforms. To address this problem, a correction procedure is designed according to the distributed geometrical configuration. As the coherence between the sub-images is adequately maintained, the data-driven motion compensation (MOCO) is readily developed to remove the residual phase errors to achieve desirable imaging performance. Simulations and raw data experiments are presented to validate the advantages of the proposed algorithm.

High-resolution radar imaging with compressive sensing (CS) is significantly important and meaningful in practical applications, such as data collection burden reduction and resource allocation scheduling in a multifunctional radar. The class of matrix completion (MC) methods is a powerful tool to directly reconstruct the missing data to be applied in sparse radar imaging, which can overcome the discrete error drawback of traditional dictionary-based CS methods. In this article, we extend the MC method to tensor completion (TC) with multidimensional data representation, and a novel manifold low-rank and sparse TC (MLRSTC) radar imaging algorithm is proposed for enhanced sparse imaging performance. In the scheme, an attractive tensor radar data model is proposed, and the low-rank tensor property is discovered by capturing the latent and intrinsic data structure in high dimensions. In particular, the low-rankness superiority of the tensor model is confirmed by both the theoretical derivation and experimental analysis. Then, the Kronecker-basis-representation (KBR)-based tensor sparsity model is applied to format the proposed MLRSTC algorithm of sparse radar imaging, which can effectively promote the reconstruction of tensor data with enhanced low-rank property. Meaningfully, the proposed MLRSTC algorithm can work well under the condition of different sparse data sampling patterns. Next, the proposed MLRSTC algorithm is efficiently solved in an iterative manner under the framework of alternating direction method of multipliers (ADMMs) by updating the involved parameters in a closed-form solution. Finally, the experiments using both electromagnetic simulation and measured data are performed to confirm the effectiveness and superiority of the proposed MLRSTC algorithm beyond state-of-the-art (SOTA).

Time-domain modulated metasurfaces (TMMs) enable passive radar jamming by dynamically altering incident waveforms. We introduce the concept of passive barrage jamming, where a TMM redistributes target energy to suppress dominant features in the synthetic aperture radar (SAR) imaging without active radiation. Traditional approaches, such as random phase modulation, can disperse target energy but lack a principle framework for shaping the SAR response, leaving detectable residual signatures. In this work, the design of TMM modulation sequences is formulated as a non-convex minimax optimization problem to improve the energy distribution of the SAR imaging. An alternating direction method of multipliers framework is developed to solve the problem efficiently under the constant-modulus constraint, with theoretical guarantees of monotonic convergence under suitable parameter settings. In SAR simulations, the optimized TMM sequences achieve a peak reduction of 70.18 dB for a point target and 48.29 dB for an extended target compared to the unmodulated baseline, outperforming both random and structured coding schemes. Experimental validation on a practical 1-bit TMM platform confirms a 12.87 dB peak reduction in one-dimensional matched filtering, despite phase quantization and hardware nonidealities. These results highlight the effectiveness of the proposed optimization approach in enhancing TMM-based barrage jamming performance, providing a robust and practical solution for radar countermeasures.

Airborne synthetic aperture radar (SAR) imagery is susceptible to non-systematic motion errors, which will definitely defocus the target image and degrade the recognition accordingly. Representations learned from conventional convolutional neural networks (CNNs) do not emphasize much about the intrinsic features of the data distribution, which collapses the purposefulness of feature extraction. To this end, a deep Merged Auto-Focusing Network (MAFNet) is proposed for adaptively removing the defocusing effect from the input SAR data used for target recognition. Specifically, we propose an end-to-end architecture consisting of one focusing module and one recognition module. The focusing module contains a U-shaped sub-network based on the cross convolution to ensure the phase history coherence of the extracted features, and a deep unfolding method is devised to improve the mathematical generalization of focusing and sparse features so as to enhance the purposefulness of feature extraction. In particular, a compact surrogate function is designed for the non-convex feature quantization problem in the focusing module, which leads to closed-form solutions. The recognition module simply consists of a classifier. By building a multivariate loss function consisting of a cross-entropy loss function and a novel focusing loss function, MAFNet achieves accurate target recognition even when the input data is contaminated by non-systematic phase errors and additive noises. MSTAR data set is utilized to validate the effectiveness of MAFNet, and comparisons with conventional algorithms are performed to demonstrate the superiority of the proposed network.

The classification performance of land features is strongly associated with the quality of samples. However, in the real world, the presence of class noise is inevitable. Class noise may seriously mislead the construction of model and limit the improvement of classifier performance. In response to this situation, a stratified mislabeled instances removal method based on the idea of density spatial clustering optimally (SD-SCM) is proposed. Herein, the convolutional neural network (CNN) is employed to evaluate the effectiveness of the proposed method.Besides, a famous noise filter, KNN-kernel Cluster based technology, is adopted to compare with SD-SCM. The results on two benchmark datasets, Indian Pines and Pavia University, demonstrate the effectiveness of the proposed noise removal method.

Owing to its superior capabilities in counter-stealth and anti-jamming, bistatic inverse synthetic aperture radar (Bi-ISAR) has garnered significant attention in both military and civilian applications. However, the variability in the bistatic angle of Bi-ISAR will lead to position-dependent defocusing and geometric distortions in the imaging result. To address these problems, a novel Bi-ISAR space-variant phase error compensation and geometric correction method based on entropy minimization is proposed. Firstly, a space-variant signal model for Bi-ISAR is derived in a short coherent processing interval (CPI). Subsequently, an optimization function is established, utilizing image entropy as the cost function. By applying the Broyden–Fletcher–Goldfarb–Shanno algorithm to solve this optimization problem, the focused imaging result and the space-variant factors can be obtained simultaneously. Utilizing the distortion relationship derived from the focused image, a range-dependent correction function is constructed and applied to perform geometric corrections. Additionally, image scaling can also be performed by using the space-variant factors. Simulated data processing results validate the effectiveness of the proposed method.

The joint observation of multiple sensors is the main means for synchronously obtaining large-coverage, high-precision, and multi-scale ocean wind and wave information. The higher the synchronization of these data in time and space, the more conducive to improving the accuracy of wind and wave information. Therefore, this paper proposes an idea of simultaneously realizing the three functions of spectrometer, scatterometer and SAR by sharing a phased-array antenna, and a very small receiving antenna is also carried to enhance the flexibility of time sequence. The constraints on the time sequence of the pulse transmission and reception for the three functions working simultaneously are derived in detail. Subsequently, a joint design method for the pulse repetition frequencies (PRFs) of three functions is introduced, and simulation verification is carried out by zebra diagrams.

This paper focuses on developing the processing algorithm applicable to the GEO SAR especially at high squint. The analytic expressions of the squint-mode wavenumber support and point spreading function are derived. Then, we propose a generalized fast Cartesian factorized back-projection algorithm, which can deal with high-squint data, and the scene coordinate system can be built with high flexibility. Especially, the optimal scene coordinate system is exploited. Finally, processing results of simulated data are presented to validate the proposed algorithm.

The Fast-Imaging Formula (FIF) enables rapid radar image simulation through One-Shot ray-tracing. However, the classical Imaging Formula is approximate results under the conditions of small angle and bandwidth. The application of FIF to wideband high-resolution radar images is subject to discussion. This study constructs simulations by employing the '6+6' Degrees of Freedom (DoF) motion model, determining the imaging plane. Utilizing the Discrete Fourier Transform (DFT), the range profile formula for large bandwidth is obtained, and the result is frequency dependent. According to the principle of spectrum correction, the integration is modified to obtain the image primitive suitable for high-resolution image simulation. The simulation results of the ship target model and the range profile analysis of the ray tube verify the effectiveness of the proposed method.

Synthetic Aperture Radar (SAR) are fundamental tools for target classification and detection in the different applicative scenarios (military, agriculture, etc…). Extracting geometrical features of a target strongly help in its detection and classification. Indeed, the extraction of Attribute Scattering Center (ASC) characteristics is widely used from improving SAR target recognition. ASC extraction is a challenging task that requires the accurate estimation of tiny details (shape, orientation, etc…) from the SAR target backscattering. In this work, the aim is to exploit the potential of deep learning for ASC extraction. Considering a simulated environment, a deep-learning based classification solution is defined for extracting the target characteristics.We proposed a multi classification heads VGG solution, which can extract scattering parameters from complex images and also guarantee the estimation accuracy.

Polarimetric Synthetic Aperture Radar (PolSAR) data plays an important role in Earth observation. In this field, deep learning (DL) method can achieve high classification performance on PolSAR image dataset and, in particular, vision transformer(ViT) has achieved significant breakthroughs. Compared with convolutional layers, ViT is able to extract global feature and find the global relationship, which can help to improve the performance of classification. The aim of this work is to exploit the potential of ViT for PolSAR classification. In this case, we propose a simple classification method based on transformer, called Pol-Trans. The PolSAR data is pre-processed to get the coherency matrix. Then the image patch of the pixel to be classified is flattened as the tokens. Finally, with the class embedding, our transformer can output the classification result of the PolSAR data. Our experiments on the ALOS2 PolSAR dataset of San Francisco shows the effectiveness of our method.

Infrared and visible images with distinct and complementary information are fused to obtain more comprehensive information. However, traditional fusion algorithms often suffer from losing details and low fusion quality. Convolutional neural networks (CNN) have been proven to possess excellent feature extraction capabilities. Therefore, a fusion algorithm for infrared and visible image fusion based on CNN and Multi-Scale Transform (MST) is proposed in this paper. In this fusion algorithm, a dual-branch CNN is employed to map the original images to the weight map. The low-pass and high-pass bands obtained through multi-scale transformation are fused separately by combining the weight map with different fusion strategies. In the experiment, five MST algorithms and 21 pairs of images are tested. The experimental results demonstrate that the proposed fusion algorithm significantly enhances the fusion quality of the original MST algorithms.

Granular computing (GrC) embraces a spectrum of concepts, methodologies, methods, and applications, which dwells upon information granules and their processing. Fuzzy C-means (FCM) based encoding and decoding (granulation-degranulation) mechanism plays a visible role in granular computing. Fuzzy decoding mechanism, also known as the reconstruction (degranulation) problem, has become an intensively studied category in recent years. This study mainly focuses on the improvement of the fuzzy decoding mechanism, and an augmented version achieved through constructing perturbation matrices of prototypes is put forward. Particle swarm optimization is employed to determine a group of optimal perturbation matrices to optimize the prototype matrix and obtain an optimal partition matrix. A series of experiments are carried out to show the enhancement of the proposed method. The experimental results are consistent with the theoretical analysis and demonstrate that the developed method outperforms the traditional FCM-based decoding mechanism.

The motion error of airborne microwave photonic synthetic aperture radar (SAR) has 2-D spatial variation characteristics, and the range spatial variant motion error (RVE) and azimuth spatial variant motion error (AVE) significantly interplay during the motion error estimation. For the RVE estimation, the standard weighted least square (WLS) algorithms are susceptible to the AVE and the moving targets. In addition, the azimuth subimage-based WLS algorithms face the problem of dominant points decreasing dramatically. This letter proposes a WLS estimation kernel based on nonlinear chirp scaling (NCS) to address the issues above. The AVE is first significantly corrected by the NCS processing, and RVE is subsequently estimated using the standard WLS kernel. In addition to eliminating the adverse effects of AVE and moving targets, the proposed method can retain sufficient dominant points to ensure the accuracy of phase gradient autofocus (PGA). The measured data processing results verify the effectiveness of the proposed method.

The vortex electromagnetic (EM) wave’s orbital angular momentum (OAM) enables beam to provide more target information, providing an extra dimension in resolution for the radar. However, different from the OAM-based communication system, the echo of OAM radar is still a plane wave, which makes the OAM radar system lack natural anti-jamming ability. In order to enhance the electronic countermeasure capability of OAM radar, this letter introduces frequency agile signals into OAM radar to increase the difficulty of jamming and detection of radar signals by jammers. The compressed sensing algorithm is used to solve the problem that OAM radar cannot directly obtain target azimuth information through Fourier transform in the OAM mode domain after introducing frequency agile signals. The simulation results demonstrate that the proposed scheme effectively improves the anti-jamming performance of the OAM radar imaging system.

In radar target recognition, anti-corner reflector interference is a critical research area. Radar cross section (RCS), commonly used radar data, serves to recognize of ship and coner reflector. However, considering the current circumstances, RCS-based ship recognition heavily relies on single-frequency multi-angle data, which limits its potential. In terms of classification methods, manual feature extraction for classification has drawbacks like subjectivity, high workload, and limited adaptability. Direct use of convolutional neural networks (CNNs) also presents limitations, including data dependency and the problem of performance upper bounds. To address these challenges, we propose a feature fusion approach for ship and corner reflector recognition using RCS under agile frequency conditions. We introduce an automatic weighting module based on channel attention mechanism for interpretable features extracted manually. These weighted interpretable features are combined with deep features from the improved Omni-Scale CNNs (OS-CNN). The experiment shows that the proposed method effectively discriminates between ships and corner reflectors and reduces reliance on observation angles during training. The overall recognition accuracy on the test set reaches 96.2%, higher than the existing methods of 3.4%~10.6%, and is robust to the fluctuation of varying sea conditions.

Interrupted-sampling and repeater jamming (ISRJ), as a novel form of active jamming, has emerged as a focal point and challenge in radar jamming countermeasures. In this letter, to enhance the suppression capability against ISRJ, we propose a recognition and suppression method based on the UNet-attention (UNet-A) semantic segmentation model. First, an attention-based direct connection structure between the encoder and decoder is designed to enhance the ability of UNet-A to identify the boundaries of jamming and the target. Then, an adaptive time-frequency (TF) filter based on the refined recognition results is designed to improve the signal-to-jamming ratio improvement factor (SJRIF). Finally, to improve the jamming suppression capability while reducing the target energy loss, an annotation method based on the target energy loss constraint criterion is proposed, and a dataset is constructed based on this. Numerical results and comparisons with the existing methods are included to demonstrate that the proposed method can effectively enhance anti-ISRJ performance.

When the locations of non-zero samples are known, the Moore-Penrose inverse (MPI) can be used for the data recovery of compressive sensing (CS). First, the prior from the locations is used to shrink the measurement matrix in CS. Then the data can be recovered by using MPI with such shrinking matrix. We can also prove that the results of data recovery from the original CS and our MPI-based method are the same mathematically. Based on such finding, a novel sidelobe-reduction method for synthetic aperture radar (SAR) and Polarimetric SAR (POLSAR) images is studied. The aim of sidelobe reduction is to recover the samples within the mainlobes and suppress the ones within the sidelobes. In our study, prior from spatial variant apodization (SVA) is used to determine the locations of the mainlobes and the sidelobes, respectively. With CS, the mainlobe area can be well recovered. Samples within the sidelobe areas are also recovered using background fusion. Our method is suitable for acquired data with large sizes. The performance of the proposed algorithm is evaluated with acquired space-borne SAR and air-borne POLSAR data. In our experiments, we use the [Formula: see text] space-borne SAR data with the size of 10000 (samples) × 10000 (samples) and [Formula: see text] POLSAR data with the size of 10000 (samples) × 26000 (samples) for sidelobe suppression. Furthermore, We also verified that, our method does not affect the polarization signatures. The effectiveness for the sidelobe suppression is qualitatively examined, and results were satisfactory.

Accurately recognizing the type of complex electromagnetic jamming is the essential prerequisite for synthetic aperture radar (SAR) anti-jamming. However, current convolutional neural network (CNN)-based SAR jamming recognition methods require balanced training samples, which contradicts the varying difficulty of acquiring various jamming types, drastically reducing the recognition accuracy and generalization ability. This article proposes a discriminative feature distance metric model, JRSNet, for jamming recognition under imbalanced training samples, by refining the jamming modulation differences in the time-frequency (TF) domain into discriminative features. Novel feature discriminative distance metric (FD2M) loss function and discriminative feature constraint module (DFCM) are put forward to guarantee JRSNet learns embedding expression paradigm from jamming TF spectrograms to discriminative features, thus eliminating the influence of imbalanced training samples. Moreover, new spatial and channel attention modules are incorporated into JRSNet to capture jamming modulation information from multiple dimensions, consequently further improving recognition accuracy. Precisely because of the captured modulation regions in feature maps by spatial attention, the proposed approach can achieve jamming suppression synchronously. Experimental results show that under imbalanced training samples, JRSNet can accurately identify multiple jamming types both within and outside the training dataset with high generalizability. Compared with the existing jamming recognition methods, JRSNet performs superior recognition while taking into account good jamming suppression performance.

The microwave photonic synthetic aperture radar (SAR) is capable of realizing large scene remote sensing observation with centimeter or even millimeter resolution, which greatly enhances the ability to acquire target information. A key issue in airborne microwave photonic SAR imaging is how to accurately correct the two-dimensional (2-D) spatial variation characteristic of the motion error. The typical two-step motion compensation (MoCo) method cannot correct the azimuth spatial variant characteristic of motion error, and the traditional subaperture methods may introduce the problems of grating lobes and image stitching. In addition, the efficiency of existing parametric full-aperture autofocus methods is usually low. To solve the above problems, a nonparametric full-aperture autofocus method based on a two-stage processing framework is proposed in this article. The first stage is to introduce a nonparametric low-order nonlinear chirp scaling (NCS) or resampling (RS) model to compensate for the low-order spatial variant motion error that accounts for the dominant component before the range cell migration correction (RCMC), which ensures that there is no significant residual RCM after the RCMC. The second stage introduces a nonparametric high-order NCS/RS model after the RCMC to compensate for the remaining high-order azimuth spatial variant phase error to achieve accurate azimuth focusing. Based on the full-aperture processing strategy, the algorithm proposed in this article avoids the problems existing in the subaperture methods. In addition, the nonparametric modeling is used throughout the autofocus processing (e.g., motion error estimation and parameter reversion of NCS/RS model), which greatly improves the efficiency of autofocus processing. The results of processing simulated and measured data verify the effectiveness of the proposed algorithm.

In recent years, the back-projection (BP) algorithm has shown good potentials in SAR focusing with nonideal conditions like nonideal trajectory and nonuniform sampling. However, when applying the original BP algorithm directly to periodic non-uniform sampling data, ghostings can be introduced into the imaging results, leading to image quality distortion. To address this issue, a novel reconstruction method based on joint sub-channel BP images is proposed. We first analyze the causes and properties of Doppler grating lobes and BP ghostings in the periodic non-uniform signals. Based on the analysis, a reconstruction process of the joint sub-channel BP images is established to achieve the purpose of suppressing ghosting. The proposed method can be smoothly combined with the Ground Cartesian back-projection (GCBP) algorithm to realize high computational efficiency. Finally, the simulation and measured data are presented to verify the effectiveness of the algorithm.

The demand for high-resolution and wide-swath (HRWS) for squint azimuth multichannel synthetic aperture radar (MSAR) platform is increasingly urgent. However, the existence of interference will seriously contaminate the squint MSAR imagery; in particular, the deceptive forwarding interference (DFI) produced by the digital radio frequency memory (DRFM) technology makes the synthetic aperture radar (SAR) imagery confusing. For this point, the research on anti-DFI of squint MSAR is discussed in detail in this article. The presence of the Doppler ambiguity of the signal increases the complexity and difficulty of the DFI suppression. To solve this problem, the difference in the space–time spectrum between the valid signal and the DFI is first analyzed, and the steering vectors of each component in the echo are mined as prior information. Based on the prior information, a two-step processing is performed: the first step is to suppress the main-lobe DFI by using subspace projection and the second step is to suppress sidelobe DFI and complete signal spectrum reconstruction with the multiple Doppler direction linearly constrained minimum variance (MDD-LCMV) beamformer. Finally, the two experiment results show the excellent performance of the proposed method in squint MSAR for suppressing DFI.

When synthetic aperture radar (SAR) works in high-squint (HS) mode, the interpolation and scaling operation of traditional frequency domain imaging algorithms will change the original structure of motion error, and lead to imaging difficulties. As a classical time-domain imaging algorithm, the back-projection (BP) algorithm is linear processing with a high tolerance for motion error. Therefore, the BP algorithm is very suitable for HS SAR imaging. To further improve the estimation accuracy of motion error, an innovative affine coordinate (AC) system is introduced into the BP algorithm. Based on this AC system, a novel 2-D autofocus algorithm is proposed, which can more accurately estimate and correct the 2-D phase error of the HS SAR BP image. The proposed algorithm has the following advantages: 1) the AC imaging grid is established according to the proposed resolution calculation method based on the BP image spectrum. Under this imaging grid, the nonsystem range cell migration (NsRCM) and the range defocus term of the BP image are significantly reduced, making the phase error estimation more accurate; 2) a spectrum alignment processing for BP image in the AC system is proposed to remove the spectrum aliasing so that the azimuth phase error (APE) can be accurately estimated; and 3) the spectrum of the BP image is orthogonal in the AC system, which makes the 2-D phase error compensation based on the established prior phase error structure more accurate. Simulation and real data experiments validate the performance of the proposed algorithm.

Spaceborne multichannel (MC) synthetic aperture radar (SAR) can achieve high-resolution and wide-swath (HRWS) imaging. However, for an MC SAR system that does not satisfy the displaced phase center antenna (DPCA) condition, ghosts will appear in the image when the traditional back projection (BP) algorithm based on direct coherent integration is applied. To obtain the image without ghosts, an MC-BP algorithm based on time-domain channel weighting is proposed in this article. Compared with the traditional BP algorithm for single-channel signal, this algorithm just adds a channel-weighting factor in coherent integration. The channel-weighting factor is determined based on the channel index and ambiguity index, which is selected based on the instantaneous space angle of the pixel. Different from the existing MC imaging methods including two steps: signal reconstruction and imaging, the proposed method fulfills the image formation in one step and thus is simpler. Moreover, it can adapt the process of MC and multimode [stripmap, spotlight, sliding spotlight, and terrain observation by progressive scans (TOPSs)] data without any extra operation. To improve the efficiency of the MC-BP algorithm and overcome the defocusing issue caused by the Earth’s curved surface in the spaceborne geometry, a fast MC-BP algorithm based on a local spherical coordinate system, i.e., MC spherical Cartesian fast BP (MC-SCFBP) algorithm, is further developed. The spaceborne SAR simulation results with 0.1-m resolution are given to verify the effectiveness of this algorithm.

Spaceborne inverse synthetic aperture ladar (ISAL) is an extension of inverse synthetic aperture radar (ISAR) in laser band. It can provide ultrahigh-resolution distance imaging of noncooperative satellites. However, the image quality will be severely degraded by radial and angular vibration phase errors, which are common in satellites. This article proposes an iterative method to jointly compensate for both types of vibration phase errors. Unlike traditional methods relying on prominent points on the target, our approach identifies prominent regions to reduce noise impact, enhancing ISAL imaging suitability. We use short-time Fourier transform (STFT) to generate time-frequency (TF) distributions of these regions. Doppler centroid tracking (DCT) yields instantaneous Doppler curves, independent of strong scattering points. Phase error estimations are derived using a weighted least-squares algorithm. An iteration process is designed to improve the estimation accuracy by adjusting the STFT window length matching with the residual phase errors during iterations. Extensive experiments on simulation and real data confirm the effectiveness and noise robustness of our proposed method.

The Doppler dispersion of the received signal is very severe when the beam width of the antenna is wide, resulting in a decrease in localization accuracy for Multiple Radiation Source Localization. To resolve the problem, we propose a passive signal focusing algorithm (PSFA) for multiple radiation sources localization based on a full aperture model. The full-aperture model overcomes the resolution degradation in conventional sub-aperture processing. In the PSFA, the residual frequency correction (RFC) eliminates the localization bias in the azimuth domain and the instantaneous Doppler compensation (IDC) resolves the Doppler dispersion in the range domain, improving localization accuracy. The matched filtering is used to complete precise azimuthal focusing and the locations are obtained according to the focusing results. Moreover, the Cramer-Rao lower bound (CRLB) for synthetic aperture localization is derived. The CRLB is essential for evaluating algorithms in theoretical studies and designing system parameters in practical applications. Finally, The CRLB, simulation, and acquired data are used to evaluate the localization performance.

In satellite synthetic aperture positioning (SAP), the curvature of the Earth’s surface and the curved orbit lead to nonlinear and asymmetric instantaneous Doppler frequencies, especially when dealing with signals of very long durations. This phenomenon significantly affects the accuracy of center frequency estimation and radiating source positioning. This study presents an ultra-high-resolution positioning algorithm designed to process ultra-long-duration data collected by a single satellite. Initially, a method for estimating the zero-Doppler moment based on sub-aperture chirp rates is proposed to obtain an unbiased estimate of the radiation source’s center frequency. Subsequently, a nonlinear instantaneous Doppler compensation method is proposed, utilizing the estimated center frequency and chirp rates to enhance the coherence of the long-duration data. Furthermore, a long coherent positioning is suggested to generate an ultra-high-resolution positioning image. Ultimately, the efficacy of the proposed algorithm is validated through simulations and acquired data.

Interferometric synthetic aperture radar (InSAR) images are severely corrupted by noise in both magnitude and phase. It is significantly essential to recover the true interferometric phase during InSAR signal processing. Usually, traditional phase denoising methods are to find homogeneous samples for filtering with the need to balance noise reduction and phase preservation, which may be a problem in dealing with topography scenes. In this article, a novel algorithm of robust nonlocal tensor decomposition (RNLTD) for InSAR phase denoising is proposed. In the scheme, a nonlocal tensor (NLT) model of the interferogram is constructed by selecting and stacking similar image patches in a nonlocal region. Benefiting from the simultaneous use of nonlocal and tensor tools, superior low-rank properties of this NLT can be acquired, which is also confirmed by numerical analysis. Then, a robust tensor decomposition algorithm is proposed to formulate the low-rank recovery of the interferogram and constrain the sparse outliers for noise reduction. Next, an alternating direction method of multipliers (ADMM) solution is applied to robustly and accurately restore the noise-reduced interferometric phase. As a result, the proposed RNLTD algorithm takes advantage of effectively capturing the phase structure in a high-dimension manner, which is helpful in phase preservation with the achievement of excellent noise reduction. Lastly, the experimental analysis using one set of simulated and two sets of measured InSAR data is performed to show the promising performance of the proposed algorithm.

With the rapid growth of the Internet of Things (IoT), fundamental security measures of wireless networks have become a basic requirement. Aiming at the identification of wireless transmitters with the same parameters, this paper proposes a specific emitter identification (SEI) method based on time-frequency feature extraction. Received signals go through preprocessing, i.e., multipath effect estimation and Doppler frequency compensation, to mitigate the channel effect. Then the time-frequency spectrum is generated and a time-frequency feature extraction network is constructed to achieve feature extraction and identification task. Real-world data are used to verify the effectiveness of the proposed method. The overall identification accuracy for stationary emitters reaches 92.9%. Besides, the proposed preprocessing method improves moving emitter identification accuracy by 12%.

Due to the unsteady motion of the platform, airborne synthetic aperture radar (SAR) images are easily smeared by motion errors. In order to obtain a well-focused image, motion error compensation is essential and autofocus methods are used widely. Different from the conventional "indirect estimation" autofocus methods, a "direct estimation" autofocus method based on multi-subaperture interference is proposed in this paper. The concept of image interference is introduced into the autofocus method for the first time. The constant term of phase error can be obtained directly through multi-subaperture interference combined with the least squares method. This method avoids error accumulation caused by subaperture phase error combination and integration operations in conventional methods. Experimental results indicate the accuracy and effectiveness of the proposed method.

Multibaseline (MB) interferometric synthetic aperture radar (InSAR) is an advanced variant of conventional InSAR that aims to enhance the accuracy and reliability of phase unwrapping (PU). Among the PU methods employed in MB-InSAR, the maximum likelihood (ML) method offers an optimal solution for phase estimation. However, its limited noise robustness has hindered its practical applicability. To address this limitation, we propose a novel approach, named InSAR phase probability density function (PDF)-to-height/deformation (PDF2HD), which leverages a newly introduced deep convolutional neural network (DCNN) with exceptional anti-noise capabilities. The PDF2HD method employs U-Net and residual network to estimate the InSAR PDF, enabling it to mitigate the influence of phase noise. We present experimental results using two simulated MB InSAR datasets to demonstrate the effectiveness of our proposed method for both digital elevation model (DEM) reconstruction and deformation monitoring.

This letter mainly considers the environmental clutter problem in distinguishing between stationary humans and animals through-wall circumstances. Focusing on the challenges of object identification in the time–frequency map, we propose a cross-scale feature aggregation (CSFA) network based on channel–spatial attention, which can improve the identification accuracy of stationary humans and animals. Specifically, life detection radar is utilized to collect data, and the time–frequency analysis method synchrosqueezing transform (SST) is used to suppress the signal noise and generate higher-resolution time–frequency maps. In order to make full use of the target information, we use a feature pyramid network (FPN) to obtain multilevel feature information maps from time–frequency maps. Then, the CSFA module is utilized to extract detailed micro-Doppler feature information from feature maps. And we use a deep convolutional neural network (CNN) to classify humans from animals. Experimental results show that the proposed model has a better performance in accuracy compared with the existing methods.

Vortex electromagnetic waves, which carry orbital angular momentum (OAM), can provide more information of the target in radar imaging and target detection. Pulsed radar is often used in current OAM radar design. However, compared with the pulsed radar, the frequency modulated continuous wave (FMCW) radar is small in size, light in weight, and low in power consumption, which can be applied into many practical scenarios. Therefore, this paper investigates into the OAM radar based on the FMCW system. It can be found that, apart from the inherent characteristics of small blind area and high measurement accuracy in FMCW system, the OAM-FMCW radar also owns an additional degree of freedom, which is independent of the frequency, finally leading to the result that the FMCW radar will not be affected by the range and velocity of the target when performing azimuth detection. Simulation results are given to validate the corresponding analysis of the proposed system.

An ensemble alignment subspace adaptation method is proposed in this letter for the cross-scene classification. It can settle the problem of both foreign objects in the same spectrum and different spectrums. The algorithm combines the idea of ensemble learning with the domain adaptive (DA) algorithm. Considering the sample imbalance problem of the original data (OD), the source data (SD) is obtained by multiple random sampling of OD according to certain rules and used as input. Then, geometric alignment and statistical alignment of SD and target data (TD) are performed to build a communal subspace, followed by the classification of TD. The classification labels are finally ensembled by counting the multiple classification results with retaining valid information. This technique can reduce the uncertainty and randomness of generating subspace projections. The experimental results on two real datasets show that the proposed algorithm has a terrific accuracy improvement compared with the traditional machine learning and DA methods.

The multiple signal classification (MUSIC) algorithm based on the orthogonality between the signal subspace and noise subspace is one of the most frequently used methods in the estimation of direction of arrival (DOA), and its performance of DOA estimation mainly depends on the accuracy of the noise subspace. In the most existing researches, the noise subspace is formed by (defined as) the eigenvectors corresponding to all small eigenvalues of the array output covariance matrix. However, we found that the estimation of DOA through the noise subspace in the traditional formation is not optimal in almost all cases, and using a partial noise subspace can always obtain optimal estimation results. In other words, the subspace spanned by the eigenvectors corresponding to a part of the small eigenvalues is more representative of the noise subspace. We demonstrate this conclusion through a number of experiments. Thus, it seems that which and how many eigenvectors should be selected to form the partial noise subspace would be an interesting issue. In addition, this research poses a much general problem: how to select eigenvectors to determine an optimal noise subspace?

In previous research, most Multi-object tracking (MOT) algorithms focus on the optical image dataset, while the Synthetic Aperture Radar (SAR) image dataset faces the characteristics of few prior samples, high false alarm rate, and various defocusing interference. On the SAR image dataset, a robust MOT algorithm is proposed to fulfill multi-ship tracking in complex imaging conditions. First, the kernelized correlation filters (KCF) algorithm, a single-object tracking algorithm, is modified and applied to reduce the impact of false alarms on tracking performance. After that, different matching strategies are adaptively adapted to associate the targets based on the three intersection patterns between the predictions and the detections, which can reduce the impact of the deviated detections. Finally, the tracker’s time limit with Gaussian distribution is proposed to improve the re-association ability after the tracking interruption caused by the defocusing. The experiment results demonstrate the robust tracking ability of the proposed MOT algorithm.

In a direction-finding process, high-resolution subspace-based algorithms are the most popular ones. It is well-known that their performance of direction of arrival estimation mainly depends on the accuracy of the signal subspace. However, the traditional methods of capturing the signal subspace do not mine the information hidden in the array output in depth, which may restrict their application to some extent. In this study, we elaborate on a novel scheme to extract the signal subspace through refinement of the correlation matrix of the array output. In the developed scheme, a collection of spatial temporal correlation matrices is firstly established. Then, we define a weighting vector for the correlation matrices, and take the weighted average of the correlation matrices as the covariance matrix of the array output. It is clear that this covariance matrix is more general than the traditional covariance matrix, and the signal subspace can be optimized through adjustment of the weighting vector. In this study, we present an optimal weighting vector by adopting the particle swarm optimization. Simulation results demonstrate that the proposed approach has better performance in root mean square error compared to the existing schemes.

Spaceborne/Airborne Bistatic Spotlight SAR(SA-BSSAR) has attracted attention recently due to its flexibility. This paper proposes a new frequency synchronization method and a two-dimensional(2-D) frequency domain imaging algorithm for SA-BSSAR. In this paper, the frequency error due to the splitting of receiver and transmitter is first obtained from the direct-path signal by using the Fractional Fourier transform(FRFT), and then the spectral analysis(SPECAN) technology is used to eliminate Doppler aliasing because of using low pulse repetition rate(PRF). Based on this, we deduce the echo signal 2-D spectrum via the method of series reversion(MSR) and analyze the range-variant under SA-BSSAR. Finally, decouple the range frequency domain from the range-variant term by chirp-z transform(CZT) to achieve scene focusing imaging. All the proposed algorithms are implemented by FFT without interpolation, which ensures efficiency. The final experimental results show that the algorithm can efficiently compensate for the frequency error, eliminate the Doppler aliasing and correct the range cell migration(RCM).

At present, the resolution of the most advanced airborne microwave photonic synthetic aperture radar (SAR) can reach the order of centimeters or even millimeters, so the two-dimensional spatial variation and two-dimensional coupling characteristics of motion error will become more serious. In this paper, based on the advantages of nonlinear chirp scaling (NCS) and resampling (RS) processing, a microwave photonic SAR full-aperture autofocus algorithm based on a cascaded NCS-RS is proposed. Firstly, the proposed algorithm combines the typical two-step MoCo and chirp-Z transform (CZT) to correct the range spatial variant characteristics of motion error. Then, a cascaded NCS-RS processing is used to correct the azimuth spatial variant characteristics of motion error, in which NCS processing is introduced before range cell migration correction (RCMC) and RS processing is introduced after RCMC. Finally, the RS in cascaded NCS-RS processing is modified to change with range to correct the range-azimuth coupling characteristic of motion error. The three steps of the algorithm belong to the full-aperture processing, which avoids the problems of grating lobes and image stitching caused by the sub-aperture algorithm. The estimation of the parameters in NCS-RS processing is modeled as a high-dimensional optimization problem. Before solving this optimization problem, it is converted to multiple one-dimensional optimization problems. The results of processing simulated and measured data verify the effectiveness of the proposed algorithm.

The wavenumber domain algorithm is an ideal solution for high-resolution and highly squinted (HRHS) synthetic aperture radar (SAR) imaging in the case of an ideal straight trajectory. However, for airborne HRHS SAR imaging, the Stolt mapping leads to nonsystematic range cell migration (NsRCM) and secondary range compression (SRC) for the HRHS SAR, which causes the HRHS SAR image to defocus severely. To obtain a well-focused HRHS SAR image, a new autofocusing algorithm for HRHS SAR imagery based on an equivalent broadside model is proposed in this article. After coarse motion compensation, linear range walk correction and azimuth resampling are employed to transform the HRHS SAR data into the equivalent broadside SAR data, which has been proved to greatly reduce NsRCM and SRC. After that, the motion error prior structure in the 2-D wavenumber domain is revealed. According to the structure, a novel 2-D wavenumber domain autofocusing algorithm is proposed by the relationship between the 1-D azimuth phase error and the 2-D wavenumber domain phase error correction. Finally, the well-focused HRHS SAR imagery is obtained. Experiments based on simulated and acquired data are carried out to verify the necessity and effectiveness of the proposed algorithm for HRHS SAR imaging.

The accuracy of a classifier, whether it is an ensemble or not, is directly influenced by the training data used in learning. In remote sensing, training data mislabeling is inevitable and faces a major challenge. This paper proposes a versatile data cleaning which handles the mislabeling problem by exploiting the ensemble concepts for identifying, then eliminating or correcting the mislabeled training data. A powerful ensemble method, random forest, is at the core of our filter design and helps to distinguish mislabeled data from uncorrupted data more accurately. The major contribution of this work lies on the explicit use of the hypothesis margin as a decision means to identify and eliminate or correct mislabeled training data in an ensemble learning framework. Another key development that makes our algorithm superior to existing approaches is a design that avoids rare class instances to be mistaken for class noise. This fundamental aspect makes our data cleaning system particularly suitable for remote sensing classification tasks which usually suffer from both mislabeling and imbalance problems. The effectiveness of our algorithm is demonstrated in performing mapping of land covers. The generalization performance of two major supervised noise-sensitive classifiers, boosting and K-nearest neighbors, is strengthened by effective class noise reduction. A comparative analysis is conducted with respect to random forest, deep convolutional neural networks, as well as two well-established ensemble-based class noise filters, the majority vote and the consensus vote filters. This analysis demonstrates that our approach is more accurate than deep convolutional neural networks (one-dimensional CNN, AlexNet, EfficientNet, ResNet50 and ShuffletNet) and the reference ensemble methods.

Devolution technique and linear inversion can be utilized in forward-looking imaging radar. However, complicated forward-looking geometry with curve trajectory for high-speed dynamic platform makes the traditional model mismatched, and the conventional method inefficient. In this paper, a superresolution forward-looking imaging algorithm based on extended sparsity and step-size adaptive matching pursuit (ESSAMP) under the optimized Doppler convolution model is proposed. The main contributions are that the multi-domain cascade phase factor (MCPF) is constructed to compensate for unified range cell migration introduced by complicated forward-looking geometry and high-order phase introduced by three-dimensional (3-D) acceleration. Then, the optimized Doppler convolution model for forward-looking imaging is derived. The modified over-complete dictionary with Doppler phase information is then fully exploited to perform the Doppler deconvolution. Besides, the forward-looking imaging problem is solved by the proposed ESSAMP method. Numerical simulations validate that the proposed algorithm is effective to reconstruct forward-looking scenarios, robust to noise, and efficient to implement.

Traditionally, when multiple targets appear within the radar beam at the same time, the range profiles of different targets are coupled together, the existing algorithms usually image each target separately due to the different motion states of the targets, making it impossible to image multiple targets simultaneously. To overcome this problem, this paper proposes a joint translational motion compensation and imaging method for multiple targets based on an integrated Kalman filter (IKF), which can realize the integration of tracking and imaging for multiple targets. Firstly, an integrated Kalman filter for wideband radar tracking is employed to predict as well as accurately estimate the next-moment motion state of multiple targets simultaneously. Then, with the precisely estimated motion state of the next moment, a joint translational compensation method with a blocked Fourier compensation matrix (BFCM) is proposed in order to compensate for the translational motion of multiple targets simultaneously, which uses the characteristics of the multi-target’s echo signal separated in the range time domain. Finally, by using the IKF and BFCM, the sequential translational motion compensation for multiple targets can be achieved, and the well-focused ISAR images for multi-target are obtained. Finally, the effectiveness of the method is verified by simulated and real data.

The Ground-Cartesian factorized back-projection (G-CFBP) is an efficient time-domain processing algorithm without image interpolation, and can realize accurate imaging for curved trajectory synthetic aperture radar (SAR). Its superiority shows good potential in airborne circular SAR (CSAR) imaging. However, the motion compensation (MoCo) based on ground Cartesian back-projection (GCBP) in the airborne CSAR is still a challenge. There are two main problems: one is that the existence of the image spectrum aliasing makes the processing of the phase error estimation inaccurate; the other is that the mapping relationship of the phase error between the image spectrum domain and the azimuth time domain still needs to be studied within GCBP processing chain. To tackle the above two problems, a novel MoCo method applicable to the GCBP algorithm is proposed and can be mainly divided into two steps: the first step is to remove the sub-aperture image spectrum aliasing by a spectrum compression operation; the second step is to establish an analytical phase error structure, which includes an auto-selection criterion of the effective support region for GCBP image. The first step ensures the accuracy of the phase error estimation, and the second step establishes the inverse-mapping relationship of the phase error between the image spectrum and azimuth time. These two procedures are both vital in improving the accuracy and robustness of the GCBP-based MoCo for the CSAR imaging. The processed results of simulated and real data are provided to verify the effectiveness of the proposed method.

Superpixel can maintain the boundary of the target and reduce the influence of speckle noise, which has been widely applied to synthetic aperture radar (SAR) image target detection. But the size of the superpixel has a great impact on the performance of superpixel-based SAR target detection algorithms. To solve this problem, we propose a multi-level ship target detection algorithm based on superpixel segmentation. Firstly, the SAR images are segmented in different levels with different superpixel sizes. Different descriptions of the SAR images are obtained in different levels. Secondly, we determine the feature of the superpixels in each level. And in order to enhance the adaptability of the proposed algorithm, we propose an adaptive distance calculation method to select the contrast superpixels in each level. Thirdly, the soft detection results are realized in each level by using the fuzzy C-means (FCM) algorithm. At last, the soft detection results obtained in different levels are fused by a new fusion strategy to achieve the final ship target detection result. The influences caused by different superxiel sizes can be effectively eased by fusion. Experiments in different SAR images have verified the effectiveness of the proposed algorithm in accurately detecting ship targets and insensitivity to the superpixel size.

Due to the sparse feature enhancement only concentrating on strong scatterers of target of interest, the conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the loss of elaborated-structure features, where weak scatterers would be overlapped by the sidelobes of strong scatterers. In this article, an elaborated-structure awareness SAR (ESA-SAR) imaging algorithm is proposed based on Hessian-enhanced total variation (HETV) regularization and cooperation. By encoding the Hessian operator onto the prior of the interested target, the high-order information connected with elaborated-structure features of interests can be captured. Different from the conventional high-order formulation that is projected onto Euclidean norm balls, the proposed algorithm uses the Schatten norm balls as the projection space, where the high-order structure tensor is established, and the elaborated-structure feature can be extracted under the intended convex regularizer. More specifically, the HETV regularizer is analytically solved under the proximal algorithm considering its nondifferentiability. An eigen-soft-thresholding (E-ST) operator is derived, so that a closed-form solution for the elaborated-structure feature can be obtained. Moreover, a synergistic multitask learning framework embedded with the sparse feature enhancement is introduced, in which the elaborated-structure feature can be solved in a cooperative manner. The cooperative learning is guaranteed in terms of both theoretical and practical aspects. Finally, both simulated and raw SAR data are processed to validate the effectiveness of the ESA-SAR algorithm. Comparisons with conventional algorithms examine the superiority of the proposed algorithm.

Accurate range model with acceleration, the coupling phase terms, and spatial-variant (SV) Doppler parameters are the main issues to be solved in high-speed-high-squint SAR (HSHS-SAR) with a curved trajectory. For these issues, an extended Omega-K (EOK) algorithm is developed in this paper. The proposed EOK algorithm mainly includes the following four aspects. Firstly, a modified range model (AMRM) considering three-dimension acceleration for a curved trajectory is established. Then, the coupling between the range and azimuth direction is removed by the modified Stolt mapping (MSM). Subsequently, an improved high-order spatial-variant (SV) phase correction approach is derived to eliminate the azimuth dependence of Doppler parameters. Finally, in order to avoid zeros-padding operation, the proposed method focuses on the sub-aperture data in the range time and azimuth frequency domain through data aligning processing. The experimental results of both simulation and real data verify the effectiveness of the proposed method.

Frequency hopping (FH) signal is one of the research hotspots of passive positioning. Aiming at the problem of FH signal localization, this paper proposes a synthetic aperture passive positioning method. The method estimates and compensates for the baseband modulation of the received signal. Then the received signal vectors are arranged into a two-dimensional matrix. The Doppler frequency of each pulse is compensated by the Doppler frequency compensate matrix. The cost function is constructed by a two-dimensional focus of the received signal, and the emitter position is directly obtained through a gird search. Simulation and experimental data verify the effectiveness of the proposed method.

In this paper, the fusion method of the weighted median filter Gram-Schmidt transform transplants to the digital signal processor (DSP). Image fusion technology has always been a key technology in the field of remote sensing image processing, but the algorithm is rarely implemented on mobile devices, so the scope of use has great limitations. The algorithm in the paper blends multispectral images and panchromatic images in the same location. The multispectral image is filtered by using a weighted median filter, and then the processed image and the panchromatic image are fused through the Gram-Schmidt transform. The filtering process reduces noise interference in the image, and the fused image combines the advantages of both images with high resolution and high color information. Due to the portability of DSP chips, the algorithm can be mounted on many mobile devices. Reduce the process of data transfer and make the image processing process more convenient.

Pine wilt disease is one of the most dangerous forest diseases. Because of its strong infectivity and harm, it is very important to find out and stop it in time. In this paper, a novel spatial-spectral random forest (SRF) algorithm for pine wilt monitoring is proposed, for solving the problem of small manual detection range, long investigation time, and untimely discovery of the diseased tree. The proposed method organically combines spatial features with spectral information to quickly and efficiently mark the location of diseased trees. In this way, the online monitoring of the target area using the data of the Beijing-2 satellite is realized. This paper analyses the location of diseased trees and provides early warnings for disease-prone trees. The accuracy of the proposed algorithm is 86.66%, by the confusion matrix analysis.

A deep convolutional neural network (DCNN) has been widely applied to the 2-D phase unwrapping (PU) in synthetic aperture radar interferometry (InSAR). Our previously-developed PGNet and BCNet outperform the model-based 2-D PU methods. However, the two networks can be further improved. As the PGNet is limited to estimating the phase gradients within ±2π, unwrapped phases can be incorrectly unwrapped sometimes. The BCNet is sensitive to the high-density distribution of the residues caused by a noisy interferogram, resulting in many isolated regions. To solve both issues, we bridge the PGNet and BCNet, studying a new DCNN-based 2-D PU framework (PG-BCNet). The results show that the PG-BCNet is more noise-robust than that of the BCNet and overcomes the limitation of the PGNet that cannot unwrap the phase gradients beyond ±2π.

Deep convolutional neural networks (DCNN)-based methods have been applied widely to ship detection in SAR images. However, most DCNN-based ship target detectors that focus on the detection performance ignore the computation complexity. We propose a lightweight anchor-free ship detection network (LASDNet) for SAR images to tackle this problem. First, a lightweight backbone utilizing a double fusion with squeeze-and-excitation-bottleneck block under the CSPNet design (CSP-DFSEB) and three pooling blocks (i.e., EVE, FCT, and ME blocks) are constructed, which achieves a balance between accuracy and efficiency. Second, a transformer-based aggregation layer conducts feature fusion. Finally, an improved one-stage anchor-free detector FCOS is presented. The analyses of the High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation (HRSID) dataset show that the proposed detector has the second least number of parameters (1.15 MB), the lowest computation complexity (1.01 GFLOPs), and the highest average precision (59.25) compared with other state-of-the-art methods.

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Due to the specific characteristics of synthetic aperture radar (SAR), there will be ambiguity interference in SAR images, resulting in low contrast of the ship target to the clutter. This letter proposes an improved super-resolution generative adversarial network (ISRGAN) based ambiguity suppression algorithm for SAR ship target contrast enhancement. The proposed ISRGAN is the first attempt of using GAN for SAR ambiguity suppression. As a post-processing procedure, it does not need prior information of SAR systems, so it can be applied to various observation scenes and different acquisition modes. The generator of ISRGAN embeds the residual dense network (RDN) to optimally fuse the global and local features of the image, and it effectively improves the completeness of the feature information used for SAR ship target contrast enhancement. The superiority of ISRGAN on ambiguity suppression is validated on the Chinese Gaofen-3 imagery.

This letter considers the coherent integration problem for a maneuvering target in low signal-to-noise-ratio (SNR) circumstances. Focusing on the range migration (RM) and Doppler frequency migration (DFM) problems caused by the motion of the target, we propose a new method called Radon-general linear chirplet transform (RGLCT). Jointly motion parameters search is employed to obtain the trajectory of the maneuvering target and the coherent integration is achieved via general linear chirplet transform (GLCT). Because of the non-sensitive-to-noise feature of the GLCT, RGLCT can realize weak target coherent integration in very low SNR environments. Multi-target detection can be achieved successfully because the GLCT is not influenced by the cross-term components. Finally, simulations and real data experiments are performed to demonstrate the effectiveness of the method. The results show that the proposed method has superior detection ability than methods including Radon-Fourier transform (RFT), and Radon-Lv’s distribution (RLVD). Both theory and experiments have fully proved that the proposed method can effectively realize coherent integration in low SNR environments.

For airborne synthetic aperture radar (SAR) imaging with high resolution and wide swath, the atmospheric turbulence may produce serious range-dependent (RD) motion error. To estimate the RD motion error, traditional methods usually first divide the range full-aperture data into multiple range blocks, and then use phase gradient autofocus (PGA) to estimate the phase error of all range blocks one by one, which is inefficient. In addition, the robustness of PGA is also affected by the number of strong scattering points. To solve these two problems, a new motion compensation (MoCo) algorithm is proposed to improve the efficiency and robustness of airborne SAR MoCo. The real data-processing results are given to verify the effectiveness of the algorithm.

Conventional beam-center approximation-based motion compensation (MOCO) algorithms fail to achieve an optimally focused image in the case of the high-resolution and high-frequency (HRHF) synthetic aperture radar (SAR) system. In this letter, a novel MOCO algorithm based on Doppler adjustment is developed with the ability to compensate the azimuth variant motion error. The change of the Doppler spectrum caused by the azimuth variant motion error is investigated and is eliminated by Doppler scaling. The proposed MOCO algorithm has dramatically improved precision when compared with the conventional MOCO methods in HRHF SAR imaging. Simulation experiments and extensive comparisons with other MOCO algorithms verify the effectiveness of the proposed algorithm.

During the last decade, synthetic aperture radar (SAR) tomography (TomoSAR) technique in 3D reconstruction is significantly improved. In particular, compressed sensing (CS) has become the best suit for urban tomographic inversion. Among CS algorithms, the orthogonal matching pursuit (OMP) combined with Bayesian information criterion (BIC) has relatively low computational complexity, however, its scatterer estimation accuracy needs to be improved. To address the problem, a prior scatterer estimation with an innovative triplethreshold detection algorithm is proposed which not only greatly reduces number of iterations but also helps suppress artifacts. Experiments on real data show that the priori overlay times maps provided by the proposed algorithm are closer to the reference maps than other two pre-estimation algorithms. Artifacts in 3D reconstruction results are well suppressed at less than half the time cost of original OMP-BIC.

Due to the requirements of portability and omni-directivity, the planar bow-tie antenna is widely used in impulse through-wall radar (ITWR). When the planar bow-tie antenna is used to radiate the ultrawide bandwidth (UWB) signal, the ringing phenomenon of the transmitted signal would be serious, which can damage the quality of radar imaging. The previous solutions for this problem are the usage of various hardware loadings; however, those loadings could cause signal energy loss and reduce the signal gain. Alternatively, this letter studies a deconvolution-technique-based signal processing approach to suppress the signal ringing. Because the proposed approach does not require any hardware loadings on the antenna, it can help improve the signal-to-noise ratio (SNR) and significantly reduce the energy loss of signal. The effectivity of this signal processing approach is verified by the radar detecting experiments.

Squint ultrahigh-resolution (UHR) synthetic aperture radar (SAR) generally uses the extended Omega-K algorithm (EOK) for range cell migration correction (RCMC). However, the extra range migration and defocusing (ERMD) will be introduced by the EOK when there are motion errors. This problem will be severe as the squint angle increases, which may sharply reduce the signal-to-noise ratio (SNR) and lead to the failure of the existing autofocus methods. In this letter, a UHR autofocus algorithm based on cascaded map-drift (MD)-PGA is proposed. The MD is used for the rough estimation to improve the SNR and the phase gradient algorithm (PGA) is used to accurately estimate the residual error based on a relatively high SNR. The new algorithm combines the advantages of MD and PGA, which can solve the problem of severe defocusing. The processing of airborne real data validates the effectiveness of the proposed algorithm.

High-resolution wide-swath (HRWS) multichannel synthetic aperture radar (SAR) system possesses a number of receiving channels along the azimuth direction, so it has the capacity of moving target indication and imaging. However, due to the radial velocity of the moving target, false targets occur in the focused image. By estimating the radial velocity and combining it with moving target imaging, false targets can be effectively suppressed. In this letter, a method of radial velocity estimation of a moving target is proposed based on the theory of subspace projection. This method does not need to estimate the real azimuth position of the moving target and can predict the processing time. Simulation and airborne measured data show the effectiveness of the proposed method.

Because fast factorized back-projection (FFBP) algorithm is not limited by the assumption of azimuth-invariant of echo signal, it has significant advantages for maneuvering platform synthetic aperture radar (MP-SAR) imaging. Due to the curved trajectory of MP-SAR, the focusing quality of the SAR image becomes very sensitive to the terrain scene elevation, and the range and angular histories are difficult to be solved in polar coordinate system for FFBP implementation. In this letter, a new image reconstruction algorithm integrated with elevation information is proposed for MP-SAR imaging with high efficiency. The proposed algorithm is based on the hybrid coordinate system which is incorporated with elevation information in the FFBP process. In the proposed algorithm, a flexible matching region is introduced to efficiently solve the range and angular histories in FFBP recursion which can reconstruct high-quality images of terrain scene with both high accuracy and efficiency. Simulation experiments are implemented and analyzed to validate the superior performance of the proposed algorithm.

To improve the life-detection radar resolution under certain hardware conditions, in this letter, a deep mutual learning generative adversarial network model (Deep Mutual GAN) is proposed. In the proposed model, the generator can improve the angular resolution of the input low-resolution radar image by five times, which is enough to meet our requirements for the resolution of life detection. We innovatively use two generators in GAN with the same network structure and make the two generators learn from each other. In this way, the learning process of a generator is not only achieved by its confrontation with the discriminator but also guided by another generator. As a result, the knowledge of the generator is no longer only obtained through its own learning; each generator learns knowledge from another generator while learning knowledge by itself. The proposed model can effectively make the convergence of GAN more stable and improves the super resolution effect. We also introduce the details of the network structure of generator and discriminator, in which residual learning and a symmetrical network structure are applied. The experimental results show that the proposed method can achieve state-of-the-art imaging effect, which is meaningful for subsequent target detection and recognition.

This letter proposes a new method for focusing ground maneuvering targets and estimating the motion parameters with a synthetic aperture radar (SAR) system. In this method, the Hough transform is applied to estimate the cross-track velocity from the slope of the range walk (RW) trajectory, and the RW and Doppler centroid shift are compensated. The second-order Keystone transform is performed to correct the additional range curve caused by the along-track velocity and cross-track acceleration. Then, we adopt the adaptive polynomial Fourier transform to estimate the second-and third-order Doppler parameters from a 1-D parameter interval, and the corresponding motion parameters are calculated. Finally, the moving target is well focused after the motion parameters compensation because the second- and third-order Doppler parameters are efficiently eliminated. Both the simulated and real data processing results are presented to demonstrate the validity of the proposed algorithm.

In terrain observation by progressive scans’ synthetic aperture radar (TOPS SAR) imaging, 3-D acceleration of maneuvering platforms makes conventional unfolding methods no longer applicable. Moreover, when the burst is long enough, the approximation of frequency-domain algorithms exceeds the margin of error. To solve these problems, a modified extended range migration algorithm (ERMA) with generalized azimuth resampling is proposed in this letter. First, the regularized 2-D spectrum is reconstructed by an uniform acceleration compensation and unfolding operation. Subsequently, generalized resampling eliminates the spatially variant acceleration phase and equalizes the Doppler parameters of targets within the same range cell. Finally, the TOPS SAR data are focused in azimuth wavenumber domain. The proposed method is supported by point target simulation.

In this letter, a novel method based on multifrequency pulse diversity (MFPD) is proposed to achieve high-resolution and wide-swath (HRWS) imaging by utilizing the displaced phase center antenna (DPCA) technique. In the MFPD mode, multiple waveforms from different frequency bands are transmitted through a single channel. Thus, within the same receive window, the echoes from different range regions correspond to different frequency bands, making it possible to separate the range ambiguous echoes in the range frequency domain. However, the azimuth sampling rate will be reduced in the MFPD mode, leading to the Doppler ambiguity. To this end, the MFPD-DPCA technique is utilized, which is capable of separating the range ambiguous echoes without loss of azimuth sampling rate. Moreover, the MFPD-DPCA technique can achieve high range resolution by spectrum splicing, which enhances the feasibility of super-high-resolution imaging. Finally, the HRWS imaging can be obtained by performing the traditional synthetic aperture radar (SAR) algorithm on the reconstructed unambiguous wideband echoes. The proposed method offers an alternative in system implementation but does not necessarily offer improved swath width over current classical HRWS-SAR methods. Numerical results corroborate the effectiveness of the considered HRWS imaging strategies in ambiguous scenarios.

Automatic target recognition has been one of the hottest research in synthetic aperture radar (SAR) data processing. Noticing that popular recognition methods cannot utilize multiple features of SAR complex data, a method fused scattering center feature and deep convolutional neural network (CNN) feature is proposed in this letter. This method contains three key parts, namely, scattering center extraction and reconstruction block, CNN feature extraction block, and final feature fusion and classification block. In this process, the scattering center feature and CNN feature are fused at the level of feature maps, which retain the space information of 2-D feature maps. What is more, the proposed half end-to-end strategy realizes the automatic update of weighting parameters in feature extraction network and subnetwork, which promotes a better recognition efficiency. Experimental results on measured SAR data show that the proposed method can achieve better accuracy than other single feature-based methods and feature fusion methods.

It is well-known that the convolutional neural network (CNN) is an effective method for synthetic aperture radar (SAR) target classification. In the convolutional layer of CNN, convolutional kernels of different sizes can extract different feature information of the target. The small-size kernel can extract the local texture feature information, and the large-size kernel can extract the global contour feature information. Traditional CNN methods usually use fixed-size kernels for convolution, and they generally lose part of the target’s feature information, resulting in the inaccurate classification of the SAR targets. This article proposes a novel CNN model based on multikernel-size feature fusion (MKSFF-CNN) for SAR target classification. MKSFF-CNN designs a convolutional methodology with a multichannel parallel topology, it uses convolutional kernels of different sizes to extract the multikernel-size deep features of the SAR target, and then, these features are fused in an optimal way to acquire the lowest loss. Moreover, MKSFF-CNN concatenates the fused features extracted by the convolutional layers of different dimensions to achieve the finest classification. MKSFF-CNN greatly elevates the feature representation completeness of the SAR targets so that more useful feature information can be exploited for SAR target classification. Undoubtedly, MKSFF-CNN can achieve a better classification performance compared with traditional CNN models with a fixed kernel size. The superiority of MKSFF-CNN is validated on the moving and stationary target acquisition and recognition (MSTAR) dataset with the detailed objective and subjective evaluation.

In order to more efficiently mine the features of polarimetric synthetic aperture radar (PolSAR) and establish a more appropriate classification model, this article proposes an improved convolutional autoencoder (ICAE) based on texture feature fusion (TFF-ICAE) for PolSAR terrain classification. First, TFF-ICAE specifically designs a multi-indicator squeeze-and-excitation (MI-SE) block and incorporates it into the CAE network. MI-SE can enhance the essential feature information while suppressing the interference information as much as possible, and it can effectively increase the between-class distance while reducing the within-class distance. Then, TFF-ICAE uses gray level co-occurrence matrix (GLCM) to capture the texture features, and it optimally fuses these texture features and the deep features extracted by ICAE to complete the multilevel feature fusion, elevating the feature representation completeness of the terrain. That is, TFF-ICAE effectively enhances the feature separation capability of different categories while greatly elevating the feature representation completeness. Experiments on the datasets of San Francisco, Oberpfaffenhofen, and Flevoland show that the proposed TFF-ICAE, respectively, achieves overall accuracies of 93.44%, 97.61%, and 97.78%, which are at least 0.92%, 1.52%, and 0.97% higher than other algorithms. Undoubtedly, the superiority of TFF-ICAE is verified on these datasets.

This article would like to provide an optimal beam steering control method for high-speed maneuvering platform synthetic aperture radar (SAR) imaging. A corresponding imaging algorithm with 3-D spatial-variation correction is proposed. First, the coordinates of beam footprint are calculated by the transition rule in each pulse repetition time (PRT). The transition rule is designed to get a unified image resolution and minimize the Doppler bandwidth. By the proposed imaging algorithm, 2-D spatial-variation envelop is corrected by azimuth keystone transform and range chirp scaling. Then the space-variant (SV) Doppler terms are compensated by frequency domain perturbation and time-domain resampling. The SV components in both the second- and third-order terms are removed. Finally, the proposed beam steering method and the imaging algorithm are verified by simulated SAR data.

Geosynchronous-Earth-orbit (GEO) synthetic aperture radar (SAR) provides excellent continuous observing capability and large swath. However, the extremely long synthetic aperture time, the curved orbit, and the nonplanar ground surface cause serious spatial variance in the GEO SAR signal. In this article, a novel fast Cartesian back-projection (BP) algorithm based on subaperture imaging on ground and multistage fusion is proposed for accurately and efficiently imaging of GEO SAR. The imaging grids are arranged on the ground surface to avoid the azimuth defocusing caused by the flat ground approximation. Then, a new two-step spectrum compression method is derived to solve the spectrum aliasing of subaperture images. Also, a multistage image fusion method is adopted to combine all the subaperture images with high efficiency. The computational complexity and the approximation of the proposed algorithm are also discussed. Simulation results verify the effectiveness of the proposed algorithm.

Airborne synthetic aperture radar (SAR) with an imaging resolution of up to 0.03 m is developed. However, the imaging process suffers from motion errors with 2-D spatial-variant characteristics that invalidate approximations suitable for motion compensation (MOCO) in a submeter resolution SAR system. To estimate and compensate for 2-D spatial-variant motion error (2-D SVME), we propose a novel two-stage processing framework for the ultrahigh-resolution microwave photonic (UHR MWP) airborne SAR imaging. In the first stage, the two-step MOCO compensates for the spatial-invariant and range-variant motion errors. Range downsampling and azimuth windowing are adopted to increase the robustness of the method. Afterward, the coupling of the 2-D SVME is greatly decreased, and a coarse-focused image is obtained. In stage two, an extended autofocusing method in the 2-D wavenumber domain based on the extended range migration algorithm (ERMA) compensates for the azimuth-variant motion errors and nonsystematic range cell migration (NsRCM) for 2-D wide-swath stripmap SAR data. After the ERMA and obtaining the coarse-focused image, the analytical structure of the residual 2-D phase error in the wavenumber domain is revealed. A nonlinear scaling equation is developed, thus relating the 1-D azimuth phase error to the 2-D phase error correction. The Ku-band stripmap UHR MWP (0.03 m) airborne SAR data are analyzed to verify the necessity and effectiveness of the proposed framework. A well-focused stripmap SAR image is obtained.

Orthogonal frequency division multiplexing (OFDM) chirp waveform is considered a good choice in the waveform design for clutter suppression, which is due to its excellent characteristics such as spectral containment, phase diversity, great dynamic spectral allocation and high degree of freedom. Considering the purpose of clutter suppression, an OFDM chirp waveform design method based on multiple groups of subchirp durations optimization is proposed to improve the output signal-to-clutter-plus-noise ratio (SCNR) in this paper. The output SCNR is closely related to the waveform spectrum, so the waveforms’ energy spectral density functions are analyzed first to build the relation between the waveform parameters and spectra. Then, the multiple groups of subchirp durations optimization based on maximum SCNR is proposed and solved by an optimization method based on the sequential quadratic programming. Finally, the proposed method is verified and the optimized waveform is compared with the general waveform and the waveform with optimized single group of subchirp durations. The results show that the waveform optimized by the proposed method has higher output SCNR and the SCNR increment compared with the general waveform increases with the number of subcarriers. Besides, the high sidelobes of the general waveform are also greatly reduced.

With the continuous progress of computer deep learning technology, convolutional neural network (CNN), as a representative approach, provides a unique solution for hyperspectral image (HSI) classification. However, the parameters of CNN can not be well-tuned when the number of training samples is insufficient, resulting in unsatisfactory classification performance. To tackle the thorny problem, a deep ensemble CNN method based on sample expansion for HSI classification is studied in this paper. Specially, spatial information is first extracted and fused with original spectral bands to help classifiers obtain discriminant spectral-spatial features. Then we use the pixel-pair feature (PPF) to expand the number of training samples so that the parameters of CNN structure can be fully trained. In addition, deep ensemble CNN is employed in this paper, enabling the trained model to obtain better generalization ability and more robust classification results. Ultimately, the proposed method is applied to classify four widely used hyperspectral data sets. Experimental results show that the studied approach yields higher classification accuracy than some CNN-based methods even under the condition of small-size training set.

Translational motion compensation plays an important role in the inverse synthetic aperture radar (ISAR) imagery. In this study, a new translational motion compensation algorithm for ISAR imaging under low signal-to-noise ratio (SNR) conditions is proposed. This method is formed based on the optimization of dynamic image sharpness metric, by which the translational parameters are accurately estimated from the returned signals. The important properties of the locally and globally optimal points of dynamic image sharpness function are proved and discussed by first using the dominant point-targets model. These properties are employed in the scheme to search for the globally optimal point and prevent the optimization being trapped at a locally optimal point. Based on the properties of optimal points and Gauss–Newton method, the algorithm to estimate the translational parameters by dynamic image sharpness metric optimization (DISMO) is devised. The DISMO can find the accurate translational parameters corresponding to the globally optimal point without being affected by local optima under low SNR conditions with high efficiency. Further, the translational compensation is completed based on the estimates. The proposed method is applied to simulated and real data. The processing results confirm the effectiveness of this new algorithm.

Phase unwrapping (PU) is a key processing step in interferometric synthetic aperture radar (InSAR). To date, a number of skillful single-baseline (SB) and multibaseline (MB) PU methods exhibiting different advantages have been proposed. However, as the basic principles of SB and MB PUs are essentially different, it is difficult to effectively and systematically compare the performance of SB and MB PUs, despite the knowledge that this type of analysis is important for allowing the ever-increasing number of InSAR practitioners to choose a suitable approach for practical applications and to optimally plan future InSAR satellite missions. Recently, the framework of the two-stage programming approach (TSPA) was proposed, and this allows for the majority of the existing SB PU methods to be transplanted into the MB domain, to allow practitioners to feasibly and comprehensively compare SB and MB PU methodologies. In this study, the digital elevation model (DEM) reconstruction accuracy is compared between the classical SB PU methods and their corresponding TSPA-framework-based MB PU methods using the$L^{p}$-norm model. Interestingly, we observed that although the number of PU residues in the MB case is larger than that in the SB case, the MB PU performance is better. The reason for this is that the type of MB residue is typically dipole, so the average length of the required MB branch-cut is shorter than that of SB. It is also demonstrated that the TSPA framework can effectively improve the PU accuracy of many existing SB PU methods when the number of input interferograms is sufficient.

Due to the constraint of minimum antenna area, 2-D ambiguity resolving is a challenging task in ship focusing of single-channel SAR mounted on high-speed maneuvering platforms. In order to accommodate the issues, a ship focusing and positioning algorithm based on 2-D ambiguity resolving is proposed. First, we analyze the constraint of minimum antenna area and the distribution of the 2-D ambiguity area. An optimal-PRF SAR concept is proposed, where the signal ambiguity is evenly distributed to the range and azimuth directions. In this concept, the timing sequence of the orthogonal phase-coded waveform is designed to make the target echoes in different regions orthogonal. Then, an orthogonal matching filter is used to suppress the signal energy in range ambiguity regions. Aiming at the target defocus and position shift caused by the Doppler ambiguity, we propose an azimuth ambiguity resolving method. The Doppler ambiguity number can be estimated by residual envelope inclination. Subsequently, the target can be relocated and accurately focused by the estimated Doppler parameters. After the operation of each target is completed, the focusing SAR image of the whole scene can be obtained. Finally, simulation results and real data processing are presented to validate the proposed algorithm.

Fast factorized back-projection (FFBP) has significant advantages for bistatic forward-looking synthetic aperture radar (BFSAR) imaging with arbitrary geometry and complex configuration. Conventional FFBP is generally based on the polar coordinate system (PCS) for recursive processing; however, it involves huge interpolations and causes computational inefficiency. In this article, a novel FFBP is developed for BFSAR focusing based on the Cartesian coordinate system (CCS), which is referred to as Cartesian fast factorized back-projection (CFFBP). In the new algorithm, a two-step spectrum correction is designed to avoid spectrum aliasing, and the Nyquist sampling requirement (NSR) for the BFSAR image spectrum can be decreased significantly. With low NSR in CCS, subimage merging can be implemented with noninterpolation processing, so that the proposed algorithm can achieve high performance in both accuracy and efficiency. Moreover, the practical problem of motion error is particularly considered in algorithm development, and well-adapted data-driven motion compensation (DDMC) is integrated with CFFBP based on which a new Cartesian fast time-domain (CFTD) processing framework is developed for BFSAR application. Promising results from both simulation and raw data experiments are provided and analyzed to validate the high performance of the proposed algorithm.

Accurate 2-D frequency spectrum (2-D FS) with acceleration, two-way range coupling terms, and spatial-variant Doppler parameters are the main problems to be solved in translational-variant bistatic forward-looking synthetic aperture radar (SAR) (TV BFSAR) with curved trajectory. For these issues, a modified omega-K imaging algorithm is derived in this article. The maximum usage of 2-D FS based on the method of series reversion (MSR) is achieved by linear range cell migration correction, and 2-D FS is linearized in bistatic range by using high-order polynomial fitting. Then, a method of azimuth resampling is introduced to implement compensation of spatial-variant Doppler parameters. Different from other bistatic omega-K methods, our newly proposed method focuses on the small-aperture data in the azimuth frequency domain to avoid azimuth aliasing without padding zeros and uses the frequency focusing position to study the model of spatial-variant phase. Simulation results and real data verify the effectiveness of the proposed method.

For ultra-high resolution synthetic aperture radar (SAR), azimuth spectrum aliasing limits the application of frequency-domain autofocus algorithms. Therefore, time-domain autofocus algorithms are often used for ultra-high resolution SAR imaging. However, the azimuth deramping operation in current time-domain autofocus algorithms may introduce an additional azimuth-dependent phase. This phase can be regarded as a part of the phase error, which significantly reduces the estimation accuracy of the phase error. To address this issue, this article proposes a new time-domain autofocus algorithm based on azimuth scaling transformation for ultra-high resolution SAR. In this algorithm, we first adopt the azimuth scaling operation to avoid the azimuth-dependent phase so that the estimation accuracy of error can be greatly improved. Then, for the azimuth-dependent shifts caused by the azimuth scaling operation, we adopt the alignment processing to remove them in azimuth-time domain. Finally, we can estimate the error accurately from the aligned signal. The simulation and measured data were processed to verify the effectiveness of the algorithm.

In the existing time-domain autofocus algorithms, the azimuth deramping operation will change the azimuth-independent phase into the azimuth-dependent phase, which may greatly reduce the accuracy of autofocus processing in squint spotlight synthetic aperture radar (SAR). In contrast, the frequency-domain autofocus algorithms can avoid this problem because it does not involve the azimuth deramping operation. However, the existing frequency-domain autofocus algorithms are proposed based on the assumption of broadside mode, which cannot be directly applied to the squint mode. Therefore, this article extends the existing frequency-domain autofocus algorithm to the squint mode combined with the extended Omega-K (EOK) algorithm. Furthermore, a space division (SD) algorithm is embedded into the proposed algorithm as preprocessing, which can effectively compensate for the azimuth-dependent motion error. The simulation and real data are processed to verify the effectiveness of the algorithm.

Considering that synthetic aperture radar (SAR) images obtained directly after signal processing are in the form of complex matrices, we propose a complex convolutional network for SAR target recognition. In this article, we give a brief introduction to complex convolutional networks and compare them with the real counterpart. A complex activation function is applied to analyze the influence of phase information in complex neural networks. Inspired by the theory of network visualization, a special kind of transfer learning based on the electromagnetic property from the attributed scattering center model is applied in our networks to modulate the first convolutional layer. The experiment shows a better performance in terms of classification accuracy compared to random weight initialization.

With the development of new active deception jamming, radar antijamming has become a major research hotspot, and the recognition of jamming type is one of its key steps. In recent years, deep learning has been successfully applied in the field of radar jamming recognition, such as convolutional neural networks (CNNs). However, it is difficult to effectively improve the accuracy of deep learning algorithms in the case of small sample. Furthermore, ensemble learning and transfer learning can effectively improve the model generalization performance. For the small sample problem, this article proposes a weighted ensemble CNN with transfer learning (WECNN-TL)-based radar active deception jamming recognition algorithm. The main idea of this method is to obtain the time–frequency distribution maps of jamming signals by the short-time Fourier transform (STFT), and then, their real parts, imaginary parts, moduli, and phases are combined differently to construct multiple datasets. Finally, an ensemble CNN (ECNN) model with weighted voting and transfer learning is constructed to realize jamming recognition. Experiments on the simulated and measured mixed datasets (including 12 types of samples) show that the proposed method can get better recognition performance than random forest (RF), support vector machine (SVM), and some CNN-based methods.

Spaceborne synthetic aperture radar (SAR) can operate at various modes, including stripmap mode, spotlight mode, sliding spotlight mode, and Terrain observation by progressive scans (TOPS) mode. These four imaging modes can be regarded as unified, differing in rotation-center ranges. To uniformly focus the data of these four imaging modes in real-time, this article proposes a real-time unified focusing algorithm (RT-UFA) for the multi-mode SAR via azimuth sub-aperture complex-valued image combining and scaling. The imaging processing can be performed while the data are being recorded. In the first stage of imaging, sub-aperture complex-valued images with relative low-resolution can be obtained by the cascade of the extended chirp scaling (ECS) and azimuth dechirp. Then, these complex-valued images are coherently combined by shifting the integer number of pixels, and thus the full-resolution image of all the recorded data can be obtained. The azimuth scaling and the pixels shifting in the RT-UFA are analyzed in detail. Simulation and SAR data results are presented to validate the analysis and RT-UFA.

Multiple azimuth channels (MACs) synthetic aperture radar (SAR) can theoretically achieve high azimuth resolution and wide swath (HRWS). Nevertheless, in practice, channel mismatch will lead to ghost or azimuth ambiguities, which will degrade the imaging quality. This article proposes a novel idea for estimating the channel mismatch of MACs SAR in the image domain. First, we found that the degree of freedom (DOF) of MACs signals doubles after signal reconstruction and imaging. As a result, when the channel number is not great enough, the subspace method for error estimation is unable to be implemented. To deal with this problem, we introduce a DOF compression method based on spectral filtering. This method can decrease the image-domain DOF. Finally, an image-domain subspace method is proposed to estimate the channel phase error, using the focused data and selecting the high SNR region of SAR images. The proposed method has advantages for the channel phase error estimation. Simulated space-borne MACs SAR data and real measured airborne SAR data are processed to demonstrate the effectiveness of the proposed method.

Unlike optical images, synthetic aperture radar (SAR) images have unique characteristics, such as few samples, strong scattering, sparseness, multiple scales, complex interference and background, and inconspicuous target edge contour information. Current SAR target detection algorithms have difficulty in balancing accuracy and speed, and the performance of these algorithms is relatively limited, thus making it difficult to deploy practical applications. To this end, this article proposes AFSar, an innovative anchor-free SAR target detection algorithm based on multiscale enhancement representation learning. First, we introduce the latest anchor-free architecture YOLOX as the basic framework. Second, to reduce the computational complexity of the model and to improve the ability of multiscale feature extraction, we redesigned the lightweight backbone, namely, MobileNetV2S. Furthermore, we propose an attention enhancement PAN module, called CSEMPAN, which highlights the unique strong scattering characteristics of SAR targets by integrating channel and spatial attention mechanisms. Finally, in view of the multiscale and strong sparse characteristics of SAR targets, we propose a new target detection head, namely, ESPHead. ESPHead extracts the features of targets with different scales by using dilated convolution with different dilated rates, so as to enhance the detection ability of the model for targets with different scales. The results of ablation experiments on the SSDD dataset show that the mAP of our algorithm reaches 0.977, while the Flops is only 9.86 G, achieving state of the art.

Inverse synthetic aperture ladar (ISAL) can perform high-resolution imaging for satellites. However, due to the short wavelength of the laser, satellite micro-vibration will introduce space-variant vibration phase error (SVVPE) and space-invariant vibration phase error (SIVVPE) in the echoes, which seriously blur the ISAL image. In this paper, we propose a noise-robust vibration phase compensation algorithm to accurately estimate and correct these two types of vibration phase errors by frequency descent minimum entropy optimization. Firstly, considering the characteristics of the micro-vibration of satellites, we establish a novel phase error model based on the Fourier series theory, which only contains low-frequency vibration components. The estimation of the phase errors is then translated into the estimation of the model’s Fourier coefficients, which can be achieved by a multi-dimensional minimum entropy optimization. After that, a frequency descent method (FD) is proposed to transform the multi-dimensional optimization into a group of two-dimensional optimizations so that the proposed algorithm can achieve monotonic iterative convergence. In addition, we introduce a solution space adaptive reduction operation to reduce the computational burden when solving the two-dimensional minimum entropy optimizations by the genetic algorithm (GA) to obtain the global optimal solution. Finally, experiments based on the simulated data and the real measured data confirm the effectiveness of the proposed algorithm. Compared with the traditional methods, the proposed algorithm achieves higher phase error estimation accuracy and better image quality.

This article presents a novel approach for the joint estimation of satellite attitude and size based on inverse synthetic aperture radar (ISAR) image interpretation and parametric optimization. The satellite’s solar panel, which is segmented from the ISAR image by employing pix2pix generative adversarial network (Pix2pixGAN), is chosen for investigation in this article due to its unique rectangular structure. We innovatively use the principal component analysis (PCA) to explore the satellite solar panel’s structural features in an ISAR imagery. The projection matrix is then established to link the extracted features and the satellite’s absolute attitude and size. Parametric optimization is established based on the relationship between the extracted features and the satellite’s absolute attitude and size. A Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based fast iterative search algorithm is employed to search the satellite’s absolute attitude and size simultaneously through an iterative approach. The simulation data are generated from actual satellite orbital parameters and computer-aided-design (CAD) models of the Aqua satellite in the experiments. Simulation experiments verify the effectiveness of the proposed method.

It is challenging to estimate satellite targets’ absolute attitude and size with limited observational data. This article proposes an innovative way to jointly estimate satellite targets’ absolute attitude and size in the 3-D stable coordinates based on inverse synthetic aperture radar (ISAR) image interpretation with only one image. By taking advantage of the rectangular solar panels commonly equipped on satellites, this article extracts solar panel’s principal components, line features, and phase features of single ISAR imagery with principal component analysis (PCA), radon transform (RT), and minimum-entropy (ME)-based autofocus method, respectively. The projection relationship between these features and the absolute attitude and size of the satellite are established separately. Through multi-features fusion, a joint parameter estimation optimization function is established. This optimization is solved iteratively by the quasi-Newton method. The attitude and size parameters can be estimated simultaneously and rapidly, realizing the satellite state estimation under limited observation data. The excellent performance of the proposed algorithm is verified through different experiments.

The nonlinear variation of viewing angles over a long duration causes a nonlinear initial phase of the received pulse in a passive positioning system with a single moving receiver. Typical positioning systems ignore the phase and perform incoherent accumulation of the long-time data, resulting in a decrease in positioning accuracy, especially at a low signal-to-noise ratio (SNR). A novel passive positioning system with a synthetic aperture technique, named synthetic aperture positioning (SAP) system, is proposed to resolve the issue. First, a new 2-dimensional (2-D) continuous sampling working model is proposed. Then, the SAP system and a cost function are given to analyze the positioning performance. Third, a positioning algorithm based on the maximum likelihood estimation (MLE) is studied to handle the cost function and position the emitter. Simulation and experimental results verify the validity and effectiveness of the proposed SAP system.

High resolution and wide-swath imaging always suffer channel errors of the multiple azimuth channels (MACs) synthesis aperture radar (SAR). This article presents an image-domain channel error estimation algorithm based on image subspace least square (ISP-LS) method and a postimaging reconstruction algorithm for MACs SAR. The proposed method mainly consists of three parts: first, preprocessing and SAR imaging; second, the ISP-LS-based channel error estimation and calibration algorithm; third, postimaging reconstruction and ambiguity suppression. The channel phase and baseline errors are joint-estimated based on image subspace after SAR imaging, providing advantages that the higher signal-to-noise ratio (SNR) regions SAR images and the subspace method can be used to achieve a more accurate estimate with a relatively low computational load. We also propose a postimaging reconstruction method for ambiguity suppression, which can realize imaging each channel data and then combining the multichannel SAR images. Simulated and acquired airborne SAR data are processed to demonstrate the effectiveness of the proposed method.

The microwave photonic (MWP) radar has the capability of generating ultrawideband (UWB) signals. It is a challenge to realize accurate extraction of attributed scattering centers (ASCs) from MWP signals. This manuscript presents a scattering parameter estimation method in the image domain for UWB MWP signals. The polar-to-rectangular resampling is required for UWB MWP signals. Therefore, a range-azimuth decoupled representation based on the ASC model is formed. The model parameter estimation is converted into an optimization problem, where the statistics of the target signal and the features of interest are modeled to provide prior information. The distribution spread maximization (DSM) and peak magnitude maximization (PMM) principles in the optimization embody this prior information. The particle swarm optimization (PSO) is utilized to search for the parameters of each ASC in the image domain. Moreover, the orthogonal matching pursuit (OMP) algorithm is introduced to avoid repeated computation. Experimental results conducted on the simulated data, XPATCH data, and real data confirm the effectiveness of the proposed method. The proposed method takes into account the specific features of UWB MWP signals, which are neglected in the existing studies. Therefore, the proposed method performs better in extracting ASC parameters from UWB MWP signals in terms of accuracy and more complete sets.

Fast factorized back-projection (FFBP) is a classic fast time-domain algorithm (FTDA), which is not limited by the assumption of azimuth-invariant of echo signal and is suitable for the bistatic synthetic aperture radar (BiSAR) process of arbitrary geometric configuration. However, when the conventional FFBP processing is employed for continuous imaging of multiple full-apertures, the processing efficiency will be decreased significantly, and difficulty will be introduced in motion compensation (MOCO) development. The main contributions in this article include the following two aspects: 1) a new FTDA framework based on FFBP implementation is developed for continuous imaging where echo data are divided into several full-aperture data blocks and then processed separately by FFBP implementation to reduce redundant BP operations for achieving high efficiency and 2) an efficient and effective data-driven MOCO methodology is developed based on the new FTDA framework for high focusing quality. In MOCO, because the phase error functions of subimages are estimated in the phase history domain from different local polar coordinate systems, these phase error functions are actually discontinuous in the spatial domain, which will bring significant discontinuity and defocusing into the final image. To address this problem, the correspondence of error functions between the spatial domain and the wavenumber domain is revealed based on which the phase error functions are reconstructed to remove the discontinuity for high focusing quality. Promising results from both simulation and raw data experiments are provided and analyzed to validate the high performance of the proposed algorithm.

Conventional sparsity-driven synthetic aperture radar (SAR) imagery proceeds via ℓ1regularization, or named by least absolute shrinkage and selection operator (LASSO). However, followed by the enhanced sparse feature, structures of the scenes or targets of interests in weak scattering are easily lost. Therefore, it becomes difficult to make use of the high-resolution SAR data, even high costs have been paid for the resolution. In this paper, a novel structure-guaranteed SAR (SG-SAR) imaging algorithm is proposed by utilizing the morphology metric for the cluster feature of the scatterers of the scenes/targets of interests. By introducing structural prior in terms of morphology norm, the intended structure features can be highlighted via convex regularization. More specifically, to accommodate to complicated scenarios or targets, the structure element in the morphology regularizer is designed to be spatially variant under structure tensor representation. Different from conventional convex optimizations, the proposed SG-SAR algorithm is solved under alternating direction method of multipliers (ADMM), which is flexible to incorporate with the super-resolution imagery. In such cases, both sparse and structural features can be simultaneously enhanced, even with limited measurements and in low signal-to-noise ratio (SNR). Superior convergence and robustness can be guaranteed. Moreover, a grouping mask scheme is used to accommodate to the complex-valued SAR data. Finally, both simulated and measured SAR data are applied for the validation. Comparisons with the conventions are performed in terms of phase transition analysis, so as to verify the superiority of the proposed algorithm both qualitatively and quantitatively.

Conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the problem of loss of structural features in weak scattering. Although there are algorithms that focus on structure enhancement, no proper balance between accuracy and efficiency can be achieved. In this article, a novel feature enhancement algorithm, named structure-awareness SAR (SA-SAR), is proposed by exploiting an emerging regularizer of structure tensor total variation (STV). By imposing the STV norm onto the prior of the scenes or targets to be imaged, the intended structure feature can be analytically solved under the proximal algorithm. More specifically, the regularization method is developed within the alternating direction method of multipliers (ADMM) framework, where closed-form proximity operators can be derived. Due to the ADMM framework, it facilitates to incorporate with more features to be enhanced in a fully synergistic way. Therefore, the$\ell _{1}$and entropy norms are involved so that the sparse and focusing features can be enhanced accordingly. Considering the coherence of the SAR data, a linear proximal operator is developed within the multitask learning framework. The unavoidable error propagations can be alleviated in a great extent. In such cases, the proposed algorithm is superior in terms of convergence and efficiency. To facilitate the implementation and computation, the STV proximal mapping is optimized under the Vieta theorem. Finally, both simulated and raw SAR data are applied to verify the effectiveness of the proposed algorithm. Comparisons with conventional algorithms are carried out to show the superiority of the proposed algorithm.

Division-of-focal-plane (DoFP) polarimeter provides a way for snapshot acquisition, making it available to simultaneously record polarization measurements at different orientations. This polarization imaging system has gained more attention in the last few years and is promising to be used in the fields of computer vision and remote sensing. However, this system suffers from the degradation of spatial resolution. To reconstruct polarization information at full resolution, polarization image demosaicking is indispensable. To address polarization image demosaicking issue while preserving the essential structure of polarization data, a sparse tensor factorization-based model is proposed. For a target cube, its similar cubes are first grouped together as a tensor. Then, its compact dictionary and sparse core tensor are learned by factorizing the tensor using sparse coding. Moreover, the correlation among different polarization orientations and the nonlocal self-similarity are adopted to boost the performance. Experimental results on synthetic and real-world data demonstrate that our proposed model outperforms several state-of-the-art methods in terms of both quantitative measurements and visual quality.

In this article, an effective clutter suppression algorithm is presented for the space-borne azimuth multiantenna high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, which is based on the null-space technique. First, the echo of bistatic geosynchronous-low earth orbit (GEO-LEO) azimuth multiantenna HRWS SAR-ground moving target indication (GMTI) system is utilized to deduce the coarse-focused image of moving targets and clutter, where the Chirp Fourier transform (CFT) in azimuth is involved. Then, the matrix form can be utilized to describe the coarse-focused image of the multiantenna SAR system and the corresponding covariance matrix can be estimated. After that, the null-space is constructed using the covariance matrix corresponding to clutter, where at least a redundant channel freedom is required. Since the null-space vector is orthogonal to signal-space vector, it can be used to suppress the clutter. As an equivalent phase is brought by the slant velocity, the moving targets’ echo can be preserved during clutter suppression. Then, the optimization and suboptimization vectors for clutter suppression are introduced. It is worth noting that the proposed clutter suppression algorithm is robust for the antenna mismatch, that is, the antenna mismatch in phase and the corresponding position error. Finally, the theoretical investigations are validated using some simulation experiments, where the experiments for bistatic GEO-LEO and single-platform azimuth multiantenna HRWS SAR-GMTI system are both included. In addition, the real measured single-platform azimuth multiantenna HRWS SAR data experiments are also performed.

As an important remote sensing means, synthetic aperture radar (SAR) has many superiorities to other sensors. How to effectively detect and locate ships in SAR images is also a popular field. In previous ship detection research, most algorithms focus on detecting the horizontal bounding box of ship targets, which ignore the rotation angle of each ships. Thus, too much background noise in the horizontal detection results makes them difficult to describe each ship accurately. Inspired by the powerful feature representation ability of convolutional neural networks (CNNs), a novel anchor-free and keypoint-based deep learning method is proposed for oriented ship detection in multiresolution SAR images. Our detector first extracts multilevel features from the input SAR image with a backbone network and feature pyramid network. Next, considering multiscale ships in multiresolution SAR images, we detect different sizes of ships on different levels of feature maps with identical head network structures. In each head network, the classification subnetwork determines each pixel in feature maps as the central pixel of this ship or not, and the regression subnetwork regresses the oriented bounding box for each ship. In the training process, the proposed oriented nonnormalized Gaussian function is used to describe the center point of ship targets, while the nonuniform weighting of the different level loss functions is used to suppress the imbalanced sample distribution. Experimental results on two authoritative SAR-oriented ship detection datasets and two Gaofen-3 images demonstrate the effectiveness and robustness of the proposed methods.

As a typical application of remote sensing technology, change detection can find the ground information changes by acquiring images of the same region at different times. The change detection using the synthetic aperture radar (SAR) with the advantages of all day and all-weather usually monitors the significant surface change, like flood disasters and earthquake deformation. However, when it comes to detecting subtle changes like vehicle traces, the traditional methods ignoring the phase coherence between image pairs cannot intensify these faint changes in the difference image. The SAR coherent change detection (CCD) based on repeat-pass repeat-geometry complex images utilizing both the intensity and phase fraction could exhibit the subtle vehicle trace in the difference image. However, the complicated background and decorrelation factors significantly affect the quality of difference images, further causing great trouble for automatic trace detection. This paper proposes the spatial feature enhanced Unet and adaptive data augmentation to realize vehicle trace detection. More specifically, the pseudo-color image is first synthesized based on a two-stage coherence estimation method. Then considering the long-continuity and parallel distribution of vehicle trace samples, the enhanced Unet is constructed by fusing spatial convolutional neural network and spatial attention mechanism. After that, the adaptation data augmentation strategy is presented by introducing manual registration errors and multiple estimation windows. Finally, the experimental results on the Sandia CCD data and our measured data demonstrate the effectiveness of the proposed method.

With the ability to locate subtle trace objects in the large-scale region, coherent change detection (CCD) has been vital research for a synthetic aperture radar (SAR) system. Finding the difference between repeat-pass repeat-geometry SAR image pair and extracting impressive trace pixels from difference image, the SAR CCD methods consist of a difference generation module and a difference analysis module. The previous CCD methods mainly pay attention to devising a sophisticated working system or an appropriate statistic model to generalize a well difference image. In this article, we introduce the deep learning method into the CCD algorithm and propose a novel trace detection paradigm, which works by hierarchically fusing the unsupervised coherent statistics model and supervised deep learning model. To be specific, the complex reflectance change detection estimator is introduced to generate a difference image and reduce the false alarm in the low clutter-to-noise region. Since the low correlation in a difference image caused by the natural factors severely affects the detection performance, the multiple statistics based on intensity summation and intensity difference are, respectively, proposed to extract water region and vegetation region and suppress the corresponding false alarm. Then the construction of the coarse-to-fine image makes use of land cover information and trace features while the compressed Unet improves the utilization efficiency of trace samples. Meanwhile, the inductive transfer learning based on unsupervised pretraining and few labeled trace samples helps to train a well detection model. Experiments on measured SAR data demonstrate the effectiveness of proposed methods.

At present, few-shot object detection research in the field of optical remote sensing images has been conducted, but few-shot object detection in the field of SAR images have rarely been explored. To this end, this paper proposes a lightweight meta-learning-based SAR image few-shot object detection method, which improves the accuracy and speed of SAR image few-shot object detection from a more balanced perspective. First, we introduce the latest FSODM method in optical remote sensing as a benchmark framework. Second, a lightweight meta-feature extractor named DarknetS is designed to enhance the feature representation of SAR images and improve detection timeliness. Furthermore, we build a new aggregation module called AggregationS, which encodes support features and query features into the same feature subspace via a novel transformer encoder. This module design can better extract the correlation and saliency between different classes in the support set, improve the detection accuracy of the query set, and enhance the detection generalization performance of new classes. Finally, we built several real-world SAR image few-shot object detection datasets to verify the effectiveness of the method. Experimental results show that FSODS can achieve a better object detection performance compared to the baseline model under the condition that only a small amount of labelled data is required for new classes of SAR image objects.

Multibaseline (MB) phase unwrapping (PU) is a vital processing procedure for MB synthetic aperture radar interferometry (InSAR) signal processing and can improve the traditional InSAR by changing the ill-posed problem to the well-posed problem. The existing research has shown that the MB PU problem can be successfully converted into an unsupervised cluster analysis problem. Using the high feature descriptiveness of the deep learning technique, an unsupervised deep convolutional neural network, referred to as CANet, is proposed to cluster all the pixels into different groups according to the input’s recognizable pattern of the ambiguity number of the MB interferometric phase. Subsequently, we extend our previous two-stage programming-based MB processing approach (TSPA) to processing MB PU on a sparse irregular network, which is established from the clustering result of CANet. Both theoretical analysis and experimental results show that the proposed method is an effective MB PU method, and its execution time is drastically lower than those of many classical MB PU methods.

Two-dimensional (2-D) phase unwrapping (PU) is a critical processing step for many synthetic aperture radar (SAR) interferometry (InSAR) applications. As is well known, the traditional 2-D PU is an ill-posed inverse problem, which means that regardless of how skillful the PU algorithm designer is, it is impossible to design an algorithm that can correctly process all the 2-D PU situations, i.e., we can only design the best PU algorithm in the statistical sense. Therefore, accumulating PU processing experience from different study cases is important for PU algorithm design. Currently, the deep learning (DL) technique provides a potential framework to accumulate processing experience, and a flood of valuable data coming from different InSAR sensors provides the ability to enable the learning-based PU technique outside the traditional model-based technique. In this article, we transform the 2-D PU problem into a learnable image semantic segmentation problem and propose a DL-based branch-cut deployment method (abbreviated as BCNet). To start, we propose the optimal branch-cut connection criterion (referred to as OPT-BC) with the reference unwrapped phase given. Next, using the relationship between the residue and branch-cut as the learning objective, BCNet is trained using the samples provided by OPT-BC to produce the branch-cut result. Finally, the traditional branch-cut method is utilized to perform the postprocessing procedure to obtain the final PU result. The experimental results demonstrate that the proposed BCNet-based PU method is a near-real-time 2-D PU algorithm, and its accuracy outperforms the traditional model- and learning-based 2-D PU methods.

Two-dimensional phase unwrapping (PU) is a classical ill-posed problem in synthetic aperture radar interferometry (InSAR). The traditional algorithmic model-based 2-D PU methods are limited by the Itoh condition, which is from the PU researchers’ experience and has critical challenges under strong phase noises or violent phase changes. Recently, advanced learning-based 2-D PU methods could break through the limitation of the Itoh condition owing to their data-driven frameworks, offering promising results in terms of both the speed and accuracy. The one-step learning-based PU method, as one of the representatives, retrieves the unwrapped phase directly from the wrapped phase through regression. However, the main disadvantage of one-step learning-based PU is that it usually blurs the output unwrapped phase due to its$L_{2}$loss, that is, it cannot guarantee the congruency between the rewrapped interferometric fringes of the PU solution and the input interferogram. To solve this problem, we propose a one-step 2-D PU method based on the conditional generative adversarial network (referred to as PU-GAN), which treats 2-D PU as an image-to-image translation problem. The generator in PU-GAN can be trained to generate the unwrapped phase through minimizing a$L_{1}$-norm loss based on a U-Net architecture, while simultaneously the corresponding discriminator can learn an adversarial loss by a structure of Patch-GAN that tries to classify if the output unwrapped phase image is real or fake. Both a theoretical analysis and the experimental results show that the proposed method outperforms the representative algorithmic model-based and learning-based 2-D PU methods.

When airborne radar platforms have a hypersonic speed, the Doppler bandwidth will be several hundred times of that from the low speed platforms. There are contradictions between pulse repeat frequency (PRF), Doppler ambiguity and range swath during the system parameters design. Azimuth multichannel technique is applied to make the PRF lower and can get a wide range swath. The equivalent phase center (EPC) under high squint (HS) mode is calculated. Then the Doppler spectrum is reconstructed by spatial filtering method with azimuth dependent channel compensation.

In this paper, a novel forest disaster detection method based on fuzzy c-means (FCM) algorithm and genetic algorithm (GA) with neighborhood information (F-NGA) is proposed. The proposed method adopts FCM to pre-classify the original data first. Then, the neighborhood factors are added into the GA model to reclassify the results of FCM. Experiment results on two multispectral Formosat-2 forest images present that our algorithm obtains better detection performance when compared with FCM, fuzzy local information c-means (FLICM) and original GA algorithm.

Recently, convolutional neural network (CNN) is widely used in hyperspectral image classification (HSIC) because of its strong self-learning and efficient feature expression ability. However, the CNN model faces the “overfitting” problem when the number of training samples is small. To improve the classification accuracy of CNN under the condition of limited training set, an ensemble CNN method based on pixel-pair and random feature selection (RFS) for HSIC is proposed in this paper. With the purpose of expanding training samples, the pixel-pair feature (PPF) is used in the presented study. Besides, ensemble CNN based on RFS is applied to further improve the classification performance. Experimental results based on two standard hyperspectral images demonstrate that the proposed method achieves better classification performance than the PPF based on CNN (PPF-CNN) and RFS based on SVM (RFS-SVM) methods.

In this paper the effectiveness of a CNN based interferometric phase unwrapping algorithm combined with phase noise filtering is analysed. In particular, the considered processing chain relies on a pre-processing step with the nonlocal filter InSAR-BM3D followed by a deep CNN solution for restoring the absolute phase. The analyses is conducted on simulated data with different coherence values and aims at comparing the performance of the unwrapping with and without the pre-processing step. This paper is the first step towards a unique deep learning solution for jointly unwrapping and restoring the absolute phase.

The existing microwave remote sensors for ocean wave observation, such as SAR and spectrometer, have their respective specialties through different measuring mechanisms. However, every individual sensor shows obvious limitations when considering advanced wide-swath complete-elements ocean wave observation. This paper proposes an integrated microwave remote sensor for double-mode ocean wave observation, and designs the main system parameters and the joint time sequence. The double-mode integrated microwave remote sensor adopts a digital array antenna, but functions as a spectrometer and a SAR simultaneously through multiple beam forming technology. The proposed double-mode integrated microwave remote sensor not only realizes simultaneous, wide-swath, relatively complete-elements, and high-accuracy observation, but also reduces the cost, and compared to traditional single satellite multi-payload systems, it is more probable.

Due to the constraint of minimum antenna area, azimuth ambiguity resolving is a challenging task in the ship focusing for single-channel synthetic aperture radar (SAR) mounted on high-speed maneuvering platforms. In order to accommodate the issues, a ship focusing algorithm based on azimuth ambiguity resolving is proposed in this paper. For ship SAR imaging with small-aperture data, the energies of different targets are separated in Doppler domain with different Doppler ambiguity numbers. Thus, the Doppler ambiguity number of a single target can be estimated by residual envelope inclination. Then, the target can be accurately focused and located at the correct position by the known Doppler ambiguity number. After the operation of each target is completed, the focusing SAR image of the whole scene can be obtained. Finally, simulation results are presented to validate the proposed algorithm.

This paper proposes to apply the local-plane model in urban tomography imaging to increase the detection probability and the regularity of the persistent scatterers (PSs). A local-plane generalized likelihood ratio test (LP-GLRT) algorithm is developed, which shows a better adaption to the nonplanar architectures and terrain when compared with the Multi-look GLRT algorithm. Experiments on Terra-SAR images are presented to validate the algorithm.

This paper deals with the combination of multi-pass images obtained by COSMO-SkyMed SAR satellite over the urban area of Napoli. The coherent processing can improve geometric resolution and the image quality of long-term coherent regions. At last, the coherently and incoherently combined images are fused tighter based on the coherence to increase the readability of the low-coherence regions.

Convolution neural network (CNN) has been successfully applied to hyperspectral image classification. However, multiclass imbalance is a major problem in the classification of hyper spectral images, and traditional CNN can hardly improve the accuracy of minority classes effectively. In this paper, a new ensemble CNN with enhanced feature subspaces (ECNN-EFSs) algorithm is proposed, which utilizes an imbalanced training set to train the model and achieves accurate classification. Experimental results on two common hyperspectral datasets show that the proposed algorithm outperforms the traditional CNN and ensemble CNN algorithms.

The fusion of hyperspectral image (HSI) and light detection and ranging (LiDAR) data can provide complementary information and improve the accuracy of land cover classification. In this paper, a novel fusion method is proposed to fuse the HSI and LiDAR dataset based on multi-scale feature extraction and total variation. In the method, the extended multi-attribute profile (EMAP) is utilized to automatically extract structural information from HSI and LiDAR elements. The extracted features are then estimated in a lower-dimensional space by multi-scale total variation (MSTV). Finally, the classification map is generated by applying random forest classifiers on the fused data. In the experiment, the performance of the proposed method is evaluated on an urban dataset of Houston. The results demonstrate that classification accuracy could be significantly improved by the proposed method compared with other methods.

In this paper, a novel Synthetic Minority Oversampling Technique based Deep Rotation Forest(SMOTE-DRoF) algorithm is proposed for the classification of imbalanced hyperspectral image data. It builds a multi -level forests cascade model by training a balanced dataset generated by SMOTE. In this model, each level of the random forest produces misclassification information of the data which are used as guidance information to adjust the sample weight adaptively for the next level. Experiment results on the hyperspectral image Indian Pines AVRIS and University of Pavia ROSIS demonstrate that the proposed method can get better performance than support vector machine, random forest, rotation forest, SMOTE combined random forest, and SMOTE combined rotation forest in imbalance learning.

This article presents a fast back-projection (BP) algorithm based on subaperture (SA) image coherent combination in a downsampled Cartesian coordinate grid for high squint diving terrain observation by progressive scans (HSD-TOPS) synthetic aperture radar (SAR) ground plane imaging. A two-step spectrum compression (SC) method is proposed to coherently combine the aliasing SA images by exploiting the relationship between the wavenumber and the image frequency. The first-step SC is introduced to align the spectrum support region centers. The second-step SC effectively corrects the space-variant spectrum inclination. The proposed algorithm does not need interpolation in the process of image combination, which ensures the accuracy and the efficiency of the algorithm. Furthermore, the SC method is well-modified to suppress the sidelobes of the focused image. Simulation and measured data processing verify the effectiveness of the proposed method.

A nonlinear trajectory of a radar platform in synthetic aperture radar (SAR) may lead to severe coupling between the range and the azimuth, which may make the ambiguity function (AF) analysis complicated. The numerical algorithm-based AF analysis may be computationally expensive, while the existing analytical algorithm-based AF analysis may cause large errors because it does not consider the coupling between the range and the azimuth. By observing that the singular value decomposition (SVD) is good to deal with the coupling problem, in this article, we propose an effective AF analysis based on SVD. The key idea is to first use a small amount of sampling points for SVD of the coupled term in the AF and then the decoupled vectors are fitted to high-order polynomials for the analytical AF calculation. It converts the double integral into the product of two single integrals in the calculation. From the proposed SVD-based AF analysis, three parameters, namely, 3-dB resolution, peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR), are then effectively computed. The simulated results verify the good performance of the proposed SVD-based AF analysis.

Ship motions during long coherent processing interval (CPI) have six degrees of freedom, and the oscillatory motions are roughly periodical. The traditional ship imaging methods usually use a short time interval to form an image, while the image quality may suffer from low resolution, poor signal-to-noise ratio (SNR), and scatter scintillation. Using a longer CPI to generate an image may improve the quality but, however, largely increase the focusing difficulty. In this article, we investigate the focusing challenges of oscillatory ships with long CPI. Through analyzing the relative motion between the radar and the ship, the properties of wavenumber domain support (WDS) and point spreading function (PSF) of oscillatory ship imaging are studied. It is illustrated that the WDS is a 3-D sparse curved surface generated by the complex relative motion, with a time-variant energy density, nonparallel spectrum boundaries, and a complex structure. The PSF of an oscillatory ship may have a 3-D resolution but also multiple high-level sidelobes. The relationship between the WDS and the nonideal PSF is illustrated with the projection slice theorem (PST). Moreover, it is discussed that the scatterers distributed on a 3-D ship cannot be focused uniformly on a 2-D imaging plane (IP) due to the variation of the slant-range plane (SRP). The projection relationships of the resolutions and focusing positions between the SRP and the IP are also derived. Simulation results are presented to validate the analyses throughout this article.

Since path curvature becomes severer for higher orbit synthetic aperture radar (SAR), the stripmap mode may not provide a reliable azimuth resolution under different look angles or at different positions. Beam steering is especially valuable herein for adjusting the azimuth resolution under different observation conditions by designing the antenna steering rate. Moreover, considering that the large range migration and center range variation in the squint mode may increase the echo length and reduce the achievable scene width, we proposed a novel 2-D beam steering (TDBS) method, which promises not only a required azimuth resolution but also a wide swath (or shortened echo length) at squint when cooperated with the variable interpulse time (VIPT) technique. The simulation results obtained under different look directions are shown to validate the effectiveness of the proposed beam controlling method.

Forward-looking correlated imaging plays an increasingly important role in modern radar imaging systems. It overcomes disadvantages of traditional side or squint synthetic aperture radar (SAR) which is dependent on specific relative motion between the radar and target scene. A new microwave forward-looking correlated 3-D imaging method based on random radiation field combined with sparse reconstruction is proposed in this article. Firstly, phased array radar (PAR) is adopted to form different and random antenna patterns. Then, combined with the compressed sensing (CS) theory, the target image can be recovered with very few samples which can break through Rayleigh resolution limitation. Furthermore, the proposed method can achieve resolution at least 5.5 times higher than real aperture imaging. To raise computation efficiency of sparse reconstruction, an improved quasi-Newton iteration method based on graphics processing unit (GPU) platform is developed. Meanwhile, a GPU-based (NVIDIA Tesla K40c) accelerated computing method can significantly reduce the processing time compared with the time given by a personal computer (PC). Both simulation and field experiment verify the validity of the proposed method.

The microwave photonic (MWP) radar technique is capable of providing ultrawide frequency bandwidth waveforms to generate ultrahigh-resolution (UHR) inverse synthetic aperture radar (ISAR) imagery. Nevertheless, conventional ISAR imaging algorithms have limitations in focusing UHR MWP-ISAR imagery, where high-precision high-order range cell migration (RCM) and phase correction are crucially necessary. In this article, a UHR MWP-ISAR imaging algorithm integrating rotation estimation and high-order motion terms compensation is proposed. By establishing the relationship between parametric ISAR rotation model and high-order motion terms, an average range profile sharpness maximization (ARPSM) is developed to obtain rotation velocity by using nonuniform fast Fourier transform (NUFFT). Second-order range-dependent RCM is corrected with parametric compensation model by using the rotation velocity estimation. Furthermore, the spatial-variant high-order phase error is extracted to compensation by the entire image sharpness maximization (EISM). A new imaging framework is established with two one-dimensional (1-D) parameter estimations: ARPSM and EISM. Extensive experiments demonstrate that the proposed algorithm outperforms traditional ISAR imaging strategies in high-order RCM correction and azimuth focusing performance.

The recent, sharp increase in the availability of interferometric data captured by different synthetic aperture radar (SAR) interferometry (InSAR) sensors poses a new scientific question that whether there is a processing framework that can combine these observations to obtain a more credible InSAR product (i.e., digital elevation model (DEM) and surface deformation estimation). In this article, we extend our previous two-stage programming-based multibaseline processing framework for combining the interferograms generated from disparate InSAR systems with different system parameters to enhance the InSAR performance at the signal processing stage. The proposed multisystem interferometric data fusion framework, abbreviated as TSDFF, includes three processing steps: multisystem interferogram registration, multisystem phase unwrapping, and absolute phase fusing. The advantage of TSDFF is that it can allow the data sets from different InSAR sensors to help each other to get rid of the limitation of the Itoh condition so that the application scope of each InSAR sensor will be effectively enlarged (e.g., measuring violent surface change or mountainous DEM). In addition, to quantitatively analyze the measurement bias robustness bound of TSDFF, the TSDFF-Fusion theorem is proposed, which offers significant application guidance for TSDFF at different noise levels. The real and simulated experimental results reveal the effectiveness of TSDFF for fusing the data sets from disparate InSAR systems.

The water body detection in high-resolution synthetic aperture radar (SAR) images is a challenging task due to the changing interference caused by multiple imaging conditions and complex land backgrounds. Inspired by the excellent adaptability of deep neural networks (DNNs) and the structured modeling capabilities of probabilistic graphical models, the cascaded fully-convolutional network (CFCN) is proposed to improve the performance of water body detection in high-resolution SAR images. First, for the resolution loss caused by convolutions with large stride in traditional convolutional neural network (CNN), the fully-convolutional upsampling pyramid networks (UPNs) are proposed to suppress this loss and realize pixel-wise water body detection. Then considering blurred water boundary, the fully-convolutional conditional random fields (FC-CRFs) are introduced to UPNs, which reduce computational complexity and lead to the automatic learning of Gaussian kernels in CRFs and the higher boundary accuracy. Furthermore, to eliminate the inefficient training caused by imbalanced categorical distribution in the training data set, a novel variable focal loss (VFL) function is proposed, which replaces the constant weighting factor of focal loss with the frequency-dependent factor. The proposed methods can not only improve the pixel accuracy and boundary accuracy but also perform well in detection robustness and speed. Results of GaoFen-3 SAR images are presented to validate the proposed approaches.

The active recognition of interesting targets has been a vital issue for synthetic aperture radar (SAR) systems. The SAR recognition methods are mainly grouped as follows: extracting image features from the target amplitude image or matching the testing samples with the template ones according to the scattering centers extracted from the target complex data. For amplitude image-based methods, convolutional neural networks (CNNs) achieve nearly the highest accuracy for images acquired under standard operating conditions (SOCs), while scattering center feature-based methods achieve steady performance for images acquired under extended operating conditions (EOCs). To achieve target recognition with good performance under both SOCs and EOCs, a feature fusion framework (FEC) based on scattering center features and deep CNN features is proposed for the first time. For the scattering center features, we first extract the attributed scattering centers (ASCs) from the input SAR complex data, then we construct a bag of visual words from these scattering centers, and finally, we transform the extracted parameter sets into feature vectors with the k-means. For the CNN, we propose a modified VGGNet, which can not only extract powerful features from amplitude images but also achieve state-of-the-art recognition accuracy. For the feature fusion, discrimination correlation analysis (DCA) is introduced to the FEC framework, which not only maximizes the correlation between the CNN and ASCs but also decorrelates the features belonging to different categories within each feature set. Experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) database demonstrate that the proposed FEC achieves superior effectiveness and robustness under both SOCs and EOCs.

The classification is one of the most important tasks of the hyperspectral remote sensing. However, the task always suffers from the curse of dimensionality which makes most classifier models disabled. In this paper, a novel ensemble method named feature separation based rotation forest (FSRoF) is proposed to avoid the influence of high-dimensionality by training a series of independent classifiers with the datasets in a low-dimensionality rotation space and using the out-of-bag instances to select the base classifiers of high quality to construct the final ensemble model. The random forest (RF) and the traditional rotation forest (RoF) are adopted as the comparisons in our experiment.

The presence of noise is often unavoidable and has been a serious nuisance factor that needs to be taken into account in the hyperspectral image classification. Effective noise handling is one of the most difficult problems in data classification. Ensemble-based filtering has been demonstrated successful in dealing with the class noise problem. In this paper, a novel two-step ensemble-based data filtering method is proposed to improve the hyperspectral image classification accuracy in the presence of class noise. The proposed method is a combination of noise redundancy classifiers and sensitive algorithms. The experimental results on two public hyperspectral datasets demonstrate the effectiveness of the proposed approach.

Fast back projection algorithm (FBPA) is commonly used for image formation of complex SAR mode. However, traditional sidelobe reduction algorithm is not applicable to remove the image sidelobes because the spectrum of the image formed by FBPA is aliased. In this paper, a novel sidelobe reduction algorithm is proposed based on spectrum compression (SC). The spectrum aliasing is eliminated by SC first. Then a modified spatial variant apodization (SVA) is used for sidelobe suppression. The mainlobe preserves without widening and the sidelobe is suppressed. Simulation and measured data processing verify the effectiveness of the proposed method.

The rapidly increasing demand on high-resolution ISAR images causes ISAR imaging to become more sensitive to errors. This leads to the invalidation of plane wave assumption and causes geometric distortion and defocus of the traditional imaging algorithms. To address this problem, a new algorithm for near-field ISAR imaging is proposed which focuses the image in two-dimensional (2-D) time-domain without distortion. To that end, a sub-aperture-based method is first applied to restore the invariance in azimuth (IIA). By fixing the rotational angle in a sub-aperture to a constant value, we proceed to compensate for the position-dependent error in the sub-aperture wavenumber domain for full image restoration. The range cell migration correction (RCMC) is performed by Stolt interpolation in the wavenumber domain. Finally, the final image is obtained by a 2-D inverse Fourier transform. Simulated and real data processing results validates the effectiveness of the proposed algorithm.

Phase unwrapping (PU) is a key step for the synthetic aperture radar (SAR) interferometry (InSAR). Single-baseline (SB) PU and multi-baseline (MB) PU are two independently developed technologies, each of which has its own advantages and disadvantages. A two-stage programming-based MB PU method (TSPA) proposed by Yu [1] establishes a connection between the MB and SB PU methods. TSPA breaks the limitation of the phase continuity assumption by using the Chinese remainder theorem (CRT), and uses the minimum-cost flow (MCF) optimization model to obtain the PU result. TSPA can be regarded as a framework for solving MB PU problems. In this paper, we studied how to transplant the infinity-norm ( L∞-norm) optimization model into TSPA framework. Under the TSPA MB PU framework, a L∞-norm based MB PU method (referred to as Inf-TSPA) is proposed to solve the problem of low PU accuracy of the L∞-norm SB PU method. The experimental results on the simulated and the realistic MB InSAR data sets verify that the performance of Inf-TSPA is significantly improved compared to the L∞-norm SB PU method.

High squint multichannel (HSMC) synthetic aperture radar (SAR) mounted on high speed maneuvering platforms is an available mode to achieve wide swath imaging. However, the traditional multichannel reconstruction methods are not suitable because of range-dependent and time-variant steering vector caused by the nonlinear trajectory. To address the issue, a novel unambiguous signal reconstruction algorithm is proposed in this paper. According to the geometry model, the properties of range-dependent and time-variant steering vector are analyzed. Then, a range-dependent and time-variant inter-channel phase compensation method is proposed to correct the space time spectrum, and the constant steering vector is obtained. Before the reconstruction, the range walk correction (RWC) is performed to remove the mismatch between the reconstruction filters and the squinted signal. Furthermore, a modified spatial domain filter is proposed to reconstruct the unambiguous Doppler spectrum. Finally, simulation results are presented to validate the proposed approach.

Ghost targets occur when moving targets are processed as stationary scene in the high-resolution and wide-swath azimuth multichannel SAR system, so moving targets require special treatment. Combining the subspace theory with the system, this paper proposes a clutter suppression method and a moving target radial velocity estimation method. The proposed clutter suppression method does not need pre-processing and it preforms better than the spacetime adaptive processing when the moving target component occupies a large proportion in the received data. And the proposed velocity estimation method has lower time complexity than the method based on the minimum entropy, so it is appropriate for the time sensitive applications. The processing of the airborne measured data verifies the effectiveness of the methods.

The curved trajectory and long synthetic aperture time of medium-earth-orbit (MEO) synthetic aperture radar (SAR) lead to a two-dimensional spatial variation in the signals. Traditional methods treat the range and azimuth variations separately, and usually suffer from high computational complexities. We investigate the Doppler rate distribution across a large scene, and exploit an optimal imaging coordinate system, in which the MEO SAR signals satisfy the azimuth-shift-invariant property. The additional processing of the azimuth spatial variation in MEO SAR imaging algorithms can be avoided, and the efficiency of the image formation processor can be improved. Finally, processing of simulated stripmap-mode data with 2-m resolution can validate the proposed algorithm.

Convolutional neural networks (CNN) can automatically learn features from the hyperspectral image data, which could avoid the difficulty of manually extracting features. However, the number of training set for the classification of hyperspectral images is always limited, making it difficult for CNN to obtain effective features and resulting in low classification accuracy. In this paper, a spectral-spatial feature (SSF) extraction based CNN method is proposed for an accurate classification with a small training set. Experimental results based on two standard hyperspectral images demonstrate the effectiveness of the proposed method.

With the development of synthetic aperture radar technology, the resolution of SAR images becomes higher accompanied by more complex clutter characteristics, which poses challenges to the detection of ship targets. To achieve efficient ship detection in high-resolution synthetic aperture radar images, a two-step ship target detection method based on a coarse-to-fine mechanism is proposed in this paper. First, the SAR image is filtered through the gravitational field method to enhance ship targets. Then the candidate targets are obtained by the improved mean dichotomy method. Finally, the kernel density estimation method is utilized to complete precise detection results. Compared with the conventional ship target detection method, the proposed method is fast and accurate. Experimental results demonstrate the effectiveness of the proposed method.

Refocusing moving targets in synthetic aperture radar (SAR) images is a challenging task Because of the unknown motion parameters of the targets. Thus, exact parameter estimation is required in the moving targets reconstructed. In order to solve the question, this paper proposed an optimization algorithm of moving targets refocusing via parameter estimation dependence of maximum sharpness principle after back projection (BP) integral. This method consists of three groups: Firstly, moving targets is detected and extracted from SAR images dependence of BP algorithm. Then, the extra phase brought by the motion parameters are obtained by driving exact function of target's 2-D wavenumber spectrum. Finally, based on the maximum sharpness principle, the motion parameters are optimized by iteratively compensating the extra phase. Moving targets can be focused well by removing the extra phase via the estimated parameters. Both simulation data and real data processing is used to demonstrate the effectiveness of the proposed algorithm.

A long synthetic aperture passive localization method for two Frequency shift keying (2FSK) signal via azimuth chirp-rate contour is proposed in this paper. By introducing synthetic aperture radar (SAR) imaging technology into passive localization, Doppler frequency change rate of received signal, which is called as azimuth chirp-rate in this paper, is estimated by azimuth focusing. Then, a grid map is formed on the ground and azimuth chirp-rate of each point is calculated to get an azimuth chirp-rate contour map. In the contour map, signal emitter is located in an azimuth chirp-rate curve in which the azimuth chirp-rate value is equal to its estimate. The azimuth chirp-rate contour map of a ground area varies with position of sensor. Therefore, two different azimuth chirp-rate curves can be obtained through different periods of a trajectory and the intersection of the two curves gives estimate of the emitter location.

This paper presents a new method for estimating the baseline error of an azimuth multi-channel SAR antenna in the SAR image domain. In this paper, the expressions of the image domain of multi-channel SAR signals with azimuth baseline errors are derived. The covariance matrix of the image domain signals is obtained by using the joint pixel method. Finally, the least-squares method of image domain is deduced to estimate the azimuth baseline of multi-channel SAR error. Simulation experiments verify the effectiveness of the proposed method.

ISAR 2D imaging is obtained by projecting the 3D structure target onto a 2D imaging plane. The angle between the imaging plane and the target spinning axis has a great influence on the projection result. Generally, this angle is neglected, which results in the target imaging has smaller size than the real target. This is not conducive to the further application of target detection and target recognition. In a short observation time, this angle cannot be estimated by monostatic radar. In order to solve such a problem, this letter proposes a method using bistatic radar to estimate the angle and accomplish accurate calibration. First, bistatic ISAR model and bistatic echo signal of spinning target are modeled. Then combining monostatic and bistatic 2D imaging, the angle can be calculated based on several prominent scatterers. Recalibration is performed based on this angle. Finally, the effectiveness of the proposed method is verified by different simulation experiments.

Spaceborne synthetic aperture radar (SAR) has a high application value in the observation of ship targets. After the ship is detected, the actual observation position of the moving ship and its motion parameters are worthy of concern, especially for some medium and large size valuable ships. In this paper, we propose a method of extracting the energy center of the ship signal trajectory to locate the ship first. Then according to the difference between the imaging position and the positioning position of the ship, the radial velocity estimation can be calculated. The proposed method does not need to construct a reference data box, and can directly locate the moving ship. The processing of the Gaofen-3 (GF-3) complex data verifies the effectiveness of the proposed method.

In microwave staring imaging, spatial-resolution of real aperture imaging is limited by actual antenna array aperture. In order to achieve high-resolution imaging of targets with sparse feature, this paper proposes a high-resolution imaging method based on temporal-spatial stochastic radiation field combining compressive sensing (CS) theory. Firstly, the formation and property of temporal-spatial stochastic radiation field are discussed. Then, signal model based on random radiation field is deduced in detail, and on this basis, high-resolution imaging method based on CS is discussed. The proposed method can distinguish targets within the beam coverage and higher quality image is achieved. Finally, numerical simulations and experiments in microwave chamber are performed to validate the method and its analysis.

There are two technical difficulties to overcome before obtaining a well-focused image from high squint (HS) synthetic aperture radar (SAR) with constant acceleration. One is effective range modeling and the other is the correction of space-variant (SV) Doppler parameters. Based on the imaging characteristics analysis, an orthogonal expansion range model (OERM) is proposed which can handle the coordinate rotation caused by range walk correction (RWC). Then a modified spectral analysis (SPECAN) with the Doppler resampling method is designed to correct the SV Doppler parameters. Finally, the proposed algorithm is verified by both simulated and real SAR data. Meanwhile, it shows an improvement in azimuth focusing quality over the reference one.

The beam steering of high-squint terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) mounted on maneuvering platforms causes azimuth spectrum aliasing and nonlinear variation of the Doppler center with target azimuth position. A joint time-Doppler deramp (JTDD) based method is proposed and mainly contains two parts. First, for the azimuth spectrum aliasing, the unfolded 2-D spectrum is obtained by a modified linear deramp function in the azimuth time domain constructed from the 3-D motion parameters. After range cell migration correction (RCMC), the data supporting area in the azimuth time domain is expanded, and thus, aliased because of the nonlinear variation of Doppler center. Then, a nonlinear deramp operation in the Doppler domain is further proposed to obtain a nonaliasing signal. The proposed algorithm is efficient with less zero-padding due to the consideration of nonlinear components of Doppler center variation. Simulation and real SAR data processing are presented to validate the proposed algorithm.

Traditional clutter suppression methods are mainly studied under the condition that the pulse repetition frequency (PRF) of the system is not less than the Nyquist frequency. Whereas in the high-resolution and wide-swath (HRWS) multichannel synthetic aperture radar (SAR) system, a low PRF is used to break through the minimum antenna area constraint. The low PRF case brings new challenges to the traditional clutter suppression methods. In this letter, a subspace projection clutter suppression method is proposed based on the fact that moving targets and the clutter consist in different signal subspaces. This method can be directly applied to the HRWS multichannel SAR system, and it shows better performance compared to the space-time adaptive processing (STAP) when the moving target components cannot be ignored in the clutter covariance matrix calculation. Simulated data and airborne measured data are processed to verify its effectiveness.

Due to the existence of vertical velocity in diving-squint synthetic aperture radar (SAR) imaging, the azimuth-shift invariance along the horizontal direction is not satisfied. This will lead to a big approximation error and influence the imaging results when the existing imaging processing methods are directly applied. In order to solve these problems, an equivalent model is first introduced to describe the motion characteristic in the diving-squint mode. By adopting this approach, the diving-squint SAR imaging can be treated as the conventional one, i.e., the height remains unchanged, with the azimuth-shift invariance satisfied along the flight direction. Based on the equivalent model, a two-step processing method for the diving-squint SAR imaging with subaperture data is proposed in this article. First, a time-scaling approach is adopted to obtain the well-focused 2-D image in the slant plane. Since the equivalent model will cause the rotation of the imaging projection plane and introduce the severe distortion in the ground imagery, a rapid geometric correction method based on inverse projection is further performed to get the ground imagery with little distortion through 2-D sinc interpolation. Simulation and real-data results validate the effectiveness of the proposed approach.

The curved trajectory and long synthetic aperture time of medium-Earth-orbit (MEO) synthetic aperture radar (SAR) lead to a 2-D spatial variation in the signals. Traditional methods treat the range and azimuth variations separately and usually suffer from high computational complexities. In this article, we investigate the Doppler rate distribution across a large scene and exploit an optimal imaging coordinate system, in which the MEO SAR signals satisfy the azimuth-shift-invariant property. Thus, the additional processing of the azimuth spatial variation in MEO SAR imaging algorithms can be avoided, and the efficiency of the image formation processor can be obviously improved. The Doppler linearization is used to address the higher-order Doppler parameters to achieve more precise focusing, and at the same time, addresses the azimuth time shift caused by the changes of signal distribution. Finally, processing results of simulated stripmap-mode data with the 2-m resolution are presented to validate the proposed algorithm.

Bistatic forward-looking synthetic aperture radar (BFSAR) breaks through the limitations of the conventional monostatic SAR on the forward-looking imaging. However, the problems of range cell migration (RCM) caused by the linear range walk and 2-D spatial variability of Doppler parameters become more serious and complicated in translational variant BFSAR. In this article, a keystone transform is introduced to correct the linear RCM. Based on the characteristics of a small aperture, the nonlinear chirp scaling (NCS) is discussed in the frequency domain to equalize the azimuth-range-dependent Doppler parameters. The improved NCS in our newly proposed BFSAR imaging algorithm, especially the re-definition of range direction and the model of spatial variant phase, differentiates this article from all the existing studies in the literature on BFSAR signal processing. Simulation results and real data processing further validate the effectiveness of the proposed algorithm.

In[1], the result of Fig. 13(b) was incorrectly provided. Now, we provide the corrected result, as shown inFig. 1.

Conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the sensitivity of nonsystematic errors and highly computational load. In this article, a cooperative multitask learning algorithm is proposed based on an autocalibrated alternating direction method of multipliers (AutoCal-ADMM) framework, by which the sparse feature of the scenes/targets-of-interests can be enhanced, and simultaneously the nonmodeled motion errors of either airborne platform or moving target can be autocalibrated in a synergistic manner. By leveraging the entropy and sparsity regularizers in the AutoCal-ADMM framework, the proposed algorithm is particularly tailored to obtain focused SAR images with enhanced sparsity. A reasonable surrogate function is designed for a convex objective function, so that an analytical proximal mapping of the entropy regularizer can be derived. Both nonsystematic range cell migration (NsRCM) and azimuthal phase errors (APEs) are concerned and coherently compensated. A linear and complex soft-thresholding operator is introduced for the sparse solution. The proposed algorithm is capable of greatly alleviating “error propagation” between multiple tasks, where an optima balance between the sparse and focusing features can be achieved. Superior performance in terms of convergence and efficiency can be guaranteed. Both raw SAR and canonical ground moving target imaging (GMTIm) data sets are processed and comparisons with conventions are performed, where the effectiveness and superiority of the proposed AutoCal-ADMM algorithm are validated.

To meet the demand for large area environment monitoring during short synthetic aperture time, this paper proposes an imaging algorithm for high squint beam steering (HS-BS) SAR platforms with constant acceleration. To achieve a good performance and high efficiency, the baseband azimuth scaling (BAS) method cannot be directly applied for the azimuth focusing. The signal is calculated based on high squint concentric circle (HS-CC) range model. The conventional derotation operation is modified by nonlinear phase and range-dependent derotation. Then the space-variant (SV) Doppler chirp rate is corrected by BAS method without sub-aperture processing.

The refraction, dispersion and reflection will occur when electromagnetic wave passing through the ionosphere, which affects the human activities of aerospace investigation and the earth environmental remote sensing severely. The total electron content (TEC) is a key to evaluate the ionosphere. This paper proposes a novel ionospheric TEC estimation method base on L-band inverse synthetic aperture radar (ISAR) signal processing, which is a two-step strategy. The coarse estimation of the first step is achieved by coherently integrated modified cubic phase function and the accurate estimation of the second step is accomplished by a minimum entropy optimization function solved by particle swarm optimization. The results of processing the simulated and measured data validate the robustness and effectiveness of the proposed method.

A suitable regularization parameter plays an important role in sparse ISAR imaging algorithms. With a proper regularization parameter, the quality of ISAR images improves. In this paper, the Homotopy re-weighted ℓ1-norm minimization is applied to ISAR imaging. This method is able to choose the accurate regularization parameter for each point in ISAR image with high efficiency. As a result, the imaging results processed by this method contain more details of the target and less artificial points. Both simulated and real data experiments validate the feasibility of the proposed method.

For the multibaseline (MB) synthetic aperture radar (SAR) interferometry (InSAR), MB phase unwrapping (PU) is an important step. With the rapid development of MB InSAR, the size of the datasets from the MB InSAR system is becoming increasingly larger. Under the situation of "bigdata", MB PU may face new problems with insufficient computing resources, or take too much running time to get the PU result. In order to deal with such case, we propose a convex hull and cluster-analysis based fast large-scale MB PU method (CCFLS) with enlightened by the single baseline (SB) PU method (CHFLS) from H. Yu [1]. CCFLS uses the clustering phenomenon of the MB residues to generate the convex hull of residues set with balance polarity, and avoids spending the computation resources on the area within the convex hull, so that the high-precision PU solution can be quickly obtained. The theoretical analysis and experiment results indicate that CCFLS can effectively reduce memory consumption and calculation time.

Ship oscillatory motions with long coherence processing interval (CPI) are complex and hard to accurately estimate. The traditional ship imaging methods usually avoid long-CPI focusing by dealing with a short observation time interval, which may make it hard to obtain a high-resolution and high-SNR image. In this paper, the mechanisms and challenges of long CPI imaging of oscillatory targets are investigated. The properties of wavenumber domain support (WDS) and point spreading function (PSF) of oscillatory targets are analyzed. It's found that the WDS spreads as a three-dimensional (3D) thin and curved sweep surface, with time-variant energy density, non-parallel boundaries and a complex structure. The PSF of an oscillatory target has a 3D resolution, but also multiple side-lobes with high level. We inspect the relationships between the properties of the WDS and the non-ideal PSF. Moreover, it's found that scatters distributed on a 3D oscillatory target cannot be focused uniformly on a predefined imaging plane (IP). The projection relationship of the target focusing positions on the slant-range plane (SRP) and the IP are also derived. The simulation results can well validate the proposed method.

This paper investigates the imaging problems in squint beam-steering synthetic aperture radar (SBS-SAR) mounted on high-speed platforms with constant acceleration. The cross-range-dependent range cell migration (RCM) is compensated by keystone transform (KT) and time domain RCM correction (RCMC). By derotation and phase compensation, the KT of Doppler folded signal is achieved without zero-padding. For azimuth processing, the signal is reconstructed by the nonlinear phase and range-dependent derotation. Then, the space-variant (SV) Doppler chirp rate is corrected by time domain azimuth nonlinear chirp scaling (ANCS). After frequency domain matched filtering, the full aperture signal is focused in the 2-D time domain. The algorithm is validated by simulated SAR data, including the evaluation of RCMC with KT, geometric correction, and the focusing performance.

The motion compensation (MOCO) for the airborne SAR with ultrahigh resolution and wide swath is required to consider the range-dependent (RD) phase error. The RD phase error may cause an RD residual-range cell migration (RCM) after the correction of RCM, which can degrade the performance of phase gradient autofocus (PGA) when estimating the phase error. In addition, because the PGA estimation is based on the strong scattering point, it may wrongly estimate the phase error for some observation scenes without strong scattering point. Alternatively, to take into account the above two problems, we study a MOCO algorithm based on two-step accuracy improvement. In the algorithm, the first step is to estimate and correct the RD residual-RCM and thus improves the accuracy of PGA. The second step is to develop a prior-information-based-weighted least square (PI-WLS) to further improve the accuracy of RD phase error estimation. Processing of airborne real data validates the effectiveness of the proposed algorithm.

The curved trajectory can lead to severely 2-D spatial-variance in spaceborne synthetic aperture radar (SAR). The azimuth-variance makes the traditional frequency domain imaging algorithms for the straight trajectory based on the assumption of azimuth translational invariance invalid. To correct the severely 2-D spatial-variance in curved trajectory spaceborne SAR, this paper studies a frequency imaging algorithm based on an optimal linear range walk correction (LRWC) preprocessing and 2-D singular value decomposition (SVD). Before the correction of the 2-D spatial-variance, an optimal LRWC preprocessing is introduced to minimize the azimuth-variance. Subsequently, a range block-SVD is proposed to correct the range-variance and, thus, achieves the accurate range cell migration correction. Finally, the azimuth tandem-SVD method is used to correct the azimuth-variance and, thus, accomplishes the azimuth compression for the whole azimuth scene. Processing of the simulated data validates the effectiveness of the proposed algorithm.

Compared with frequency-domain algorithms, time-domain algorithms (TDAs) can achieve image focusing under the conditions of arbitrary trajectories and large integration angles. However, the interpolations in both range and bearing angle directions are required in the coordinate transformation of fast TDAs, which inevitably increases the computational load and introduces interpolation errors. In this paper, a fast time-domain imaging and corresponding autofocus method based on the hybrid coordinate (HC) system is proposed. First, the interpolation operation is optimized in the process of fast TDAs. It transforms the 2-D interpolation into 1-D interpolation only in the bearing angle direction, which improves the execution efficiency and reduces the interpolation error. Next, a 3-D trajectory deviation estimation method based on Gauss-Newton iteration is investigated for the motion compensation in the HC system. By iterative optimization, the 3-D motion errors during the flight are estimated accurately, and the space-variant phase error is compensated precisely. This method has good robustness and universality. Simulation results and real data processing demonstrate the effectiveness and the practicability of the method presented in this paper.

A squinted observation geometry along with long integration time significantly aggravates the range walk and spatial variation of a medium-earth-orbit (MEO) synthetic aperture radar (SAR) signal. Variable pulse repeating frequency (PRF) is recommended to avoid the blockage in echo recording and save storage space. The existing wavenumber algorithms cannot handle the nonlinear and range-azimuth-coupled spatial variation (RACSP) over a large scene. In this paper, we propose a modified Stolt mapping method along with a modified joint time and Doppler resampling (JTDR) for highly squinted MEO SAR data processing. An azimuth timescale transformation is used to deal with the nonlinear spatial variation of the azimuth frequency-modulation (FM) rate. An extended Omega-K is used to linearize the range frequency and achieve range cell migration correction (RCMC). To address the RACSP, the Doppler is linearized in the range-Doppler domain using a range-dependent Doppler scale transformation. The computational complexity and geometry distortion correction (GDC) are also discussed. Simulation results are shown to verify the effectiveness of the developed focusing approaches.

Focusing moving targets with frequency-domain algorithms may suffer from azimuth spectrum not entirely contained within a pulse-repetition frequency band, which may lead to degraded detection performance due to distributing the energy to the artifacts. In order to avoid this problem, a refocusing algorithm after back-projection integral is proposed. The main idea is first to uniformly and coarsely focus moving targets for detection, and then extract the detected targets for refocusing. By deriving the exact analytic expression of the wavenumber spectrum, motion parameter estimation and motion compensation are directly carried out on the 2-D wavenumber domain of the small-sized extracted data, which involves fast Fourier transform and Inverse Fast Fourier Transform operations only with no interpolation, thus reduces the computational complexity. Then, the final refocused image of the moving target is achieved. Refocusing results of both airborne and spaceborne synthetic aperture radar data are shown to validate the effectiveness of the proposed method.

Accurate range modeling, cross-range-dependent range migration, and space-variant Doppler parameter are main issues to be solved in processing high-squint synthetic aperture radar (SAR) data acquired from maneuvering platforms. A frequency domain backprojection algorithm, based on local Cartesian coordinate (LCC) and subregion range cell migration correction, is proposed to deal with these problems. With the proposed algorithm, the range model is built in an LCC system to accurately match the signal characteristics after range walk correction. Then, the compensation of cross-range-dependent range migration is implemented based on properly divided subregions after azimuth spectrum filtering. Finally, the space-variant Doppler parameter and higher order phase terms are coherently integrated in range-Doppler domain to get the focused subregion images with full resolution of the synthetic aperture. The final image of the entire scene is obtained by directly connecting all subregion images. The results of simulated and real SAR data validate the proposed algorithm.

Due to the very high orbit, the apparent features of geosynchronous synthetic aperture radar (GEOSAR) are the curved trajectory and long integration time, which can lead to severe coupling between the azimuth and the range directions and, therefore, complicates the resolution evaluation. The traditional analytical approach based on the 2-D division may produce large resolution error, and the numerical approach may suffer from huge computation burden. Therefore, an analytical resolution evaluation approach for GEOSAR based on the local feature of the ambiguity function is studied in this paper. The proposed approach is validated with simulation data to be of high efficiency and accuracy. In addition, the proposed approach is also demonstrated to be capable of evaluating the resolution for other complex platforms, and of evaluating the 3-D resolution of a SAR system.

The available focusing algorithms for medium-Earth-orbit (MEO) SAR are all based on the complex nonhyperbolic range equation, which may make it more difficult in imaging processing. In this paper, we model the range equation as the standard hyperbolic form based on the motion compensation (MOCO) principle. However, the conventional two-step MOCO may introduce azimuth spectrum expansion due to the potential large motion error, which can lead to severe azimuth ambiguity. To resolve this problem, we develop an omega-K algorithm based on a modified two-step MOCO and an adaptively straight equivalent (ASE)-velocity model. The algorithm is implemented through three-step processing: 1) the modified two-step MOCO does not compensate for the quadratic motion error (the main factor for the spectrum expansion); 2) an ASE-velocity model is introduced to compensate for the quadratic motion error; and 3) an extended Stolt mapping is proposed to perform the accurate range cell migration correction, and the tandem singular value decomposition-nonlinear chirp scaling algorithm is to correct the azimuth-variant phase error and to perform the azimuth compression. Processing of simulated data and airborne SAR real data validates the effectiveness of the proposed algorithm.

A two-stage sparse structure representation algorithm which can preserve the manifold structure of the data is proposed for synthetic aperture radar target configuration recognition in this paper. Manifold structure of the data is preserved by two stages. In the training stage, taking advantage of both the sparse representation (SR) and manifold learning, local structure of the data is preserved in the reconstruction space, where SR-based recognition is realized. In the testing stage, two structure preserving factors based on the testing samples are embedded into the SR model to enhance structure preserving performance. The first one is constructed to preserve the local structure of the testing samples, which can guarantee the samples that are close to each other in the original space will also be close to each other in the sparse space. And the second one is established to preserve the distant structure of the testing samples, which can ensure the samples that are far from each other in the original space will also be far from each other in the sparse space. Manifold structure of the data is well captured and preserved by two stages. Experimental results on the moving and stationary target acquisition and recognition database demonstrate the effectiveness of the proposed algorithm.

Image formation of large scenes is still challenging in medium-earth-orbit (MEO) synthetic aperture radar (SAR) due to the existence of severe 2-D space variance. In this paper, the properties of space variance are analyzed in detail, and then a variable-coefficient fourth-order range model is adopted to model the space-variant range history of every target in a large scene accurately. A method integrating a modified chirp scaling algorithm with joint time-Doppler resampling is proposed to address the range-variant range cell migration, as well as the azimuth-variant frequency-modulation rate and higher order Doppler parameters. The computational burden and alternative implementation approaches are also discussed. Finally, processing of simulated data for MEO SAR with 2-m resolution is presented to validate the proposed algorithm.

In this paper, the physical optics method is employed to study the problem of characteristics analysis and image processing for full-polarization synthetic aperture radar (SAR), where electromagnetic scattering from a flat horizontal perfect electric conducting (PEC) reflector is involved. The model of the full-polarization SAR echo from a small region of flat horizontal PEC reflector is deduced based on the dyadic Green's function theory. With the available echo model, image processing and results are presented for the full-polarization SAR. For the horizontal co-polarization channel, the amplitude of imaging result descends with the squint angle, where the well-focused imaging result can always be obtained. On the contrary, the amplitude of cross-polarization channel imaging results is increased with the squint angle. When the radar works in the side-looking mode or low-squint mode, the cross-polarization channel echo cannot be well focused. For the high-squint mode, it can be well focused. For the horizontal co-polarization channel, the amplitude of well-focused result can be approximately regarded as a constant for all squint angles. The effectiveness of the characteristics analysis results is demonstrated via simulated and real measured Ka-band airborne full-polarization SAR data.

Similar to the conventional squint mode synthetic aperture radar (SAR) imaging processing, the estimation of Doppler centroid is one key problem for the low-squint-mode (within the range of [-5°, 5°]) multichannel in an azimuth high-resolution and wide-swath (HRWS) SAR system. In this paper, based on the electromagnetic scattering model, a novel weighted Doppler centroid estimation approach is proposed for the multichannel in the azimuth HRWS (MC-HRWS) SAR system. First, Maxwell's equations are employed to derive the echo model for the multichannel SAR system. Then, an improved backscattering model is presented based on the small perturbation method and the available echo model, which is adopted to produce the weights for Doppler centroid estimation. More importantly, in order to improve the precision of Doppler centroid estimation, a weighted ambiguity-free Doppler centroid estimation approach is proposed, where the range-variant characteristic of Doppler centroid is employed to resolve the ambiguity number of Doppler centroid, and the echoes from different range bins with different signal-to-noise ratios are considered. In addition, the Cramer-Rao low bound of the estimated Doppler centroid is also discussed in this paper. The effectiveness of the proposed Doppler centroid estimation approach is verified via simulated and real measured low-squint-mode MC-HRWS SAR data.

Chaotic‐based random stepped frequency signal is applied in the multiple‐input‐multiple‐output through‐the‐wall detection radar (MIMO‐TWDR)recently. When the frequency modulation (FM) sequence of transmission signal is controlled by the chaotic signal, the single‐frequency interference such as the power harmonics sneaking into the phase detector becomes periodical and therefore can be filtered in frequency domain. However, the target echo signal becomes random after chaotic modulation, where the matched filter usually is unable to be realised by Fourier transform and consequently the envelope of direct wave after the phase detector varies stochastically and is difficult to be eliminated by an analogue filter. The FM sequence of random disorganising cannot meet the demand of filtering out the single‐frequency interference and direct wave simultaneously. Therefore, a new method of FM sequence optimisation of chaotic‐based random stepped frequency signal based on genetic algorithm is proposed to solve these problems in this study. Simulations show that the optimised stepped frequency signal possesses the advantages of both chaotic‐based random and linear stepped frequency signal. The proposed scheme achieves excellent performance on direct wave and single‐frequency interference suppression and target detection. Moreover, it can avoid the interference between transmission antennas of MIMO radar.

For a multiple-input and multiple-output synthetic aperture radar, stepped frequency chirps can be used to generate high-resolution range profiles (HRRPs) by using spectrum synthesis. However, the presence of channel phase errors may degrade the performance of HRRP synthesis. This letter presents a channel error estimation method to address this problem. First, to obtain a focused subband image, a range phase adjustment by contrast enhancement algorithm is proposed to estimate inner-channel high-order phase errors. Second, a sidelobe balanced model is established to estimate constant phase error from the relationship between the balanced sidelobe and constant phase; the constant phase error can be directly obtained in an efficient manner. Experimental analysis using real data demonstrates the effectiveness of the proposed method.

In this letter, a novel estimation approach for the Doppler chirp rate and baseline in azimuth is proposed for the multichannel in azimuth high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system. First, a range-invariant Doppler chirp rate estimation approach is developed based on a map drift algorithm and correlation function method. Then, a weighted local maximum-likelihood approach is adopted to obtain an accurate estimation of the range-variant Doppler chirp rate. With an accurate Doppler chirp rate, the baseline in azimuth can be estimated, which is developed from the correlation function between the echoes of adjective channels. The proposed approaches are successfully applied to process real five-channel HRWS SAR echo data, demonstrating the efficacy of the proposed methods.

For the ultrahigh-resolution airborne stepped-frequency synthetic aperture radar, very large synthetic bandwidth and very long integration time may lead to a 2-D space-variant (SV) motion error when the aircraft flies off the ideally straight trajectory due to the atmospheric turbulence. This new type of error complicates the motion estimation and motion compensation (MOCO). For the motion estimation, we present a jointly 2-D SV motion error estimation method to simultaneously consider the range-variant motion error and the azimuth-variant motion error. For the MOCO, we propose a 2-D SV-MOCO method. The method is implemented through three processing steps: 1) two-step MOCO for the space-invariant motion error and the range-variant phase error; 2) range block-based chirp-z transform (CZT) for the range-variant envelope error; and 3) range block division for the range-dependent azimuth-variant phase error based on the azimuth subaperture method. Finally, processing of simulated data and real data validates the proposed methods.

In a synthetic aperture radar (SAR) system, the radar platform may move with a curved trajectory due to the existence of vertical velocity and acceleration, which may result in the failure of the conventional imaging methods. In order to deal with this problem, this paper proposes an improved wavenumber-domain imaging algorithm for high-resolution-high-squint SAR with a curved trajectory. It mainly includes three aspects. First, a modified equivalent range model for a curved trajectory is derived. Second, an improved wavenumber domain imaging algorithm based on the proposed range model is analyzed in detail. Finally, the imaging distortion caused by vertical velocity and acceleration is corrected via geometry and inverse projection. Simulated results and Ku-band real SAR data processing are used to validate the proposed model and imaging algorithm.

In very high resolution spaceborne-squinted sliding spotlight synthetic aperture radar, the traditional imaging algorithms based on the equivalent squint range model (ESRM) cannot be applied, because the ESRM model is inaccurate in this case. For this problem, this paper proposes a squint equivalent acceleration range model to precisely take into account the spaceborne-squinted curved orbit. Then a full-aperture squint-imaging algorithm is proposed based on this new range model, which can handle the azimuth variation of the equivalent velocity and the range variation of the 2-D frequency spectrum. The results of the simulation validate the effectiveness of new range model and imaging algorithm.

Comparing to the original backprojection (BP) algorithm, the fast factorized backprojection algorithm accelerates enormously by dividing the synthetic aperture into many small pieces and finishes the BP integral in many stages. Numerous two-dimensional (2-D) image interpolation operations are utilized to raise accuracy. In this letter, a new factorized backprojection algorithm is proposed where no interpolation is involved. Coarse images are reconstructed and fused precisely in Cartesian coordinates. A spectrum compression method is introduced to decrease the Nyquist sampling requirement in cross-range direction for efficiency. Simulation and real-data experiments prove the validity and superiority of the proposal.

Because the anisotropy characteristic shows some properties of scattering centers, an algorithm for extracting anisotropy characteristic of scattering centers when wide-angle SAR is used is proposed in this paper. Firstly, the echo from a single scattering center is analyzed. Based on the analysis, the estimation of anisotropy characteristic is transformed into an inverse problem of solving a single scattering center's amplitude by using identity matrix as orthonormal basis. Then the inverse problem is solved under the constraint that amplitude of scattering centers should be continuous. Finally anisotropy characteristic is extracted from the solution of the inverse problem. Estimating results of both Matlab simulated and electromagnetic computation data validate that the algorithm is precise and robust. Moreover, the proposed method is more efficient in comparison with traditional methods.

Motion errors are inevitably introduced when data is acquired and considerably degrade the image quality in terms of geometric resolution, radiometric accuracy and image contrast, especially in high‐resolution spotlight synthetic aperture radar (SAR) imagery. In this study, the authors present a novel two‐dimensional (2D) autofocus algorithm directly inserted into polar format algorithm, which compensates the envelop error and the phase error sequentially. A coarse error correction is first performed by global positioning system or inertial navigation system in the range‐compressed domain, then a new envelop compensation strategy, stage‐by‐stage approach, is designed, obtaining promising results for removing range cell migration after 2D interpolation. Additionally, a weighed contrast enhancement autofocus algorithm based on spatially variant model is developed to compensate for the residual phase error, which remarkably improves the estimation accuracy. The presented algorithm is very robust to deal with substantial errors over a variety of scenes even in conditions of homogenous areas with no prominent point scatterers and enables the utilisation of fast Fourier transform. The experimental results obtained by the proposed algorithm confirm that the analysis extends well to realistic situations.

The ultralong synthetic aperture time and a very large scene cause severe 2-D spatial variation in geosynchronous synthetic aperture radar. The range variation was corrected using the range cell migration equalization and the modified chirp scaling function. The azimuth variation correction with the singular value decomposition and the azimuth nonlinear scaling was studied. The validity of the proposed imaging algorithm has been assessed. Satisfactory results were obtained in the removal of the azimuth variation, and the focusing of point targets from a synthetic aperture up to 1000 sand a scene of 150 km (azimuth) × 130 km (range).

Signal bandwidth and integration time are two significant parameters of a geosynchronous synthetic aperture radar (SAR) sensor. They directly determine the resolution characteristic of SAR imagery. Because their contributions to the ground impulse response width (IRW) curve (consists of −3-dB resolutions in every direction) are severely coupled, an analytical extraction method of the ground IRW curve is studied to analyze the coupling characteristic. Due to the coupling, the IRW curve is generally spatially variant and, thus, can degrade the quality of SAR imagery. To minimize the variation, the two parameters are optimized. However, the optimized signal bandwidth is found to be satellite position varied, which complicates the system design. To solve this problem, a parameter optimization model is built to obtain one optimal signal bandwidth as well as to decrease the variation.

An efficient and accurate inverse extended omega-K algorithm (IEOKA) is proposed to simulate synthetic aperture radar (SAR) raw data with trajectory deviations. Different from the traditional inverse omega-K algorithm that assumes an ideal flight trajectory, the IEOKA not only recovers the range cell migration accurately but also considers the motion errors including both range and phase errors due to the use of inverse extended Stolt interpolation. Furthermore, the azimuth dependence of the motion errors is discussed. A beam division method based on frequency division technique is presented to generate the azimuth-dependent phase error more accurately. The accuracy and effectiveness of the proposed algorithm have been verified using the generated SAR raw data consisting of the azimuth-dependent motion error.

For high-squint synthetic aperture radar (SAR) with curved trajectory, the traditional hyperbolic range model (HRM) is inaccurate, and the variation of velocity caused by constant acceleration in azimuth dimension cannot be ignored. Thus, the traditional omega-K imaging algorithms based on HRM are no longer available. For this problem, this letter proposes an improved range model, which can precisely take account for the curved trajectory. Then, a modified omega-K algorithm based on this new range model is proposed for high-squint SAR imaging. Simulation results validate the effectiveness of the improved range model and imaging algorithm.

In this letter, several important aspects are addressed for highly squinted synthetic aperture radar data with subaperture (SA) processing. First, the influences of the reference linear range walk correction and the resulting residual Doppler centroid are addressed. Then, the differences between the full-aperture data and SA data in the azimuth focusing are investigated in detail. Finally, a novel frequency-nonlinear-chirp-scaling algorithm with an addition of highly varying residual Doppler centroid correction over azimuth is proposed to equalize the azimuth-variant Doppler parameters so that the uniform azimuth processing can be realized. Compared with the previous works, this addition greatly improves the focused quality of the final image. Airborne real data processing validates the effectiveness of the proposed algorithm.

This letter proposes a new motion compensation approach for high-squint and high-resolution airborne spotlight synthetic aperture radar (SAR) integrated with polar format algorithm (PFA). The motion error is coarsely compensated by means of measuring systems. In PFA, the expressions of the line-of-sight polar interpolation are first explicitly revealed, and the nonsystematic range cell migration (NsRCM) induced by residual motion error is also corrected, which paves the way for autofocus application. After the NsRCM correction, the enhanced total least square estimator-based phase adjustment by contrast enhancement (PACE) algorithm is considered to account for the residual phase errors extended to the range-dependent case. The processing results of airborne SAR real data are presented to demonstrate the validity of the proposed algorithm.

The imagery of highly squinted synthetic aperture radar mounted on maneuvering platforms with nonlinear trajectory is a challenging task due to the existence of acceleration and the cross-range-dependent range migration and Doppler parameters. In order to accommodate these issues, a frequency-domain imaging algorithm based on tandem two-step nonlinear chirp scaling (TNCS) with small aperture is proposed. For the cross-range-dependent range cell migration (RCM) caused by the linear range walk correction and acceleration, the first-step NCS is introduced to suppress this dependence and realize the unified RCM correction. Based on the differences between full-aperture and small-aperture data in the cross-range processing, the second-step NCS is introduced in frequency domain to equalize the cross-range-dependent Doppler parameters, for cross-range processing is more sensitive to the cross-range dependence than range processing. Furthermore, a novel geometric correction method based on inverse projection is utilized to eliminate the negative effects caused by the imaging processing. Simulation results and real data processing are presented to validate the proposed approach.

In synthetic aperture radar (SAR) images, moving targets are usually smeared and/or imaged at incorrect positions due to the target motions during the SAR integration time. Moreover, since a high-resolution wide-swath SAR system is operated with a rather low pulse repetition frequency, a moving target will cause multiple ghost targets in the reconstructed SAR image. A new space-time adaptive processing framework is proposed in this paper for removing moving target artifacts in SAR images. In this new framework, the dynamic steering vector concept is proposed. In addition, this paper develops a moving target processing scheme for clutter suppression and moving target imaging and location for a high-resolution wide-swath SAR system. Finally, we locate the well-focused moving targets at the stationary scene image without any disturbing artifacts. The simulated and real data are used to validate the effectiveness of our proposed method.

Due to the skew data support region (DSR) of the 2-D wavenumber spectrum for high-squint synthetic aperture radar (HS-SAR), the conventional w-k algorithm cannot fully utilize the DSR and thus degrades the resolution if simply taking a rectangle region. Meanwhile, for the small-aperture data, direct azimuth imaging in the distance domain will lead to serious aliasing. A modified ω-k algorithm is derived in this paper to solve these problems. The maximum usage of DSR is achieved by the coordinate rotation. As for the azimuth dependence, the method of azimuth resampling is used to get the uniform focusing. Different from the traditional w-k method, the modified w-k algorithm focuses the small-aperture data in the azimuth wavenumber domain by SPECAN processing, which avoids padding a large number of zeros when imaging in the azimuth distance domain. Simulated and real-data results show the validity and effectiveness of the presented algorithm.

In spaceborne synthetic aperture radar, the sliding spotlight mode can acquire high resolution and large azimuth scene size simultaneously. However, when the resolution is very high and the azimuth scene size is large, the traditional hyperbolic range model (HRM) is inaccurate and the variation of the equivalent velocity in azimuth dimension cannot be ignored. Thus, the traditional imaging algorithms based on HRM are no longer available. For this problem, this paper proposes an equivalent acceleration range model, which can precisely take into account the spaceborne curved orbit. Then, velocity scaling algorithm based on this new range model is proposed to meet the needs of very high resolution and large azimuth scene size. The results of the simulation validate the effectiveness of the new range model and the imaging algorithm.

In this paper, for a multichannel synthetic aperture radar-ground moving target indication (SAR-GMTI) system, a new high-accuracy focusing and relocating method using instantaneous interferometry, i.e., carrying out interferometry operation in the azimuth time domain before focusing, is proposed. One of the key steps of this method is to perform instantaneous interferometry to get accurate equivalent cross-track velocity (ECV) estimation for cross-track motion compensation. After that, the signal from a moving target is concentrated in range, and along-track motion compensation becomes convenient. Motion compensation transforms a moving target into a stationary one; thus, the conventional SAR imaging algorithm can be applied to focus the moving target. Finally, a strategy for accurately relocating a moving target is presented. The processing results of simulated and measured data illustrate the effectiveness of the proposed method.

In the current scenario of high-resolution inverse synthetic aperture radar (ISAR) imaging, the non-cooperative targets may have strong maneuverability, which tends to cause time-variant Doppler modulation and imaging plane in the echoed data. Furthermore, it is still a challenge to realize ISAR imaging of maneuvering targets from sparse aperture (SA) data. In this paper, we focus on the problem of 3D geometry and motion estimations of maneuvering targets for interferometric ISAR (InISAR) with SA. For a target of uniformly accelerated rotation, the rotational modulation in echo is formulated as chirp sensing code under a chirp-Fourier dictionary to represent the maneuverability. In particular, a joint multi-channel imaging approach is developed to incorporate the multi-channel data and treat the multi-channel ISAR image formation as a joint-sparsity constraint optimization. Then, a modified orthogonal matching pursuit (OMP) algorithm is employed to solve the optimization problem to produce high-resolution range-Doppler (RD) images and chirp parameter estimation. The 3D target geometry and the motion estimations are followed by using the acquired RD images and chirp parameters. Herein, a joint estimation approach of 3D geometry and rotation motion is presented to realize outlier removing and error reduction. In comparison with independent single-channel processing, the proposed joint multi-channel imaging approach performs better in 2D imaging, 3D imaging, and motion estimation. Finally, experiments using both simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

In this paper, an interpolation-free fast factorized back-projection (IF-FFBP) algorithm is proposed for high-resolution spotlight synthetic aperture radar (SAR) processing. Different from the original FFBP utilizing two-dimensional image-domain interpolation for sub-aperture fusion, IF-FFBP finishes the image merging steps using chirp-z transform and circular shifting. Under the restriction of the applicable scope, IF-FFBP yields enhanced efficiency over the 4 times upsampling interpolation based FFBP, and keeps the high precision simultaneously. Finally, Real-data experiment verifies the efficiency superiorities of the FIM-FFBP.

This paper proposes a novel deramp space-time adaptive processing (Deramp-STAP) method for synthetic aperture radar (SAR) systems to achieve effective clutter suppression. Compared with the traditional STAP, the proposed method can overcome the spectral wrapping problem of a moving target from a Doppler shift. And the computational complexity can be drastically reduced because we only need to consider the baseband velocity of the moving target, here the range of the baseband velocity is much smaller than that of the real target velocity, for clutter suppression in this method. Simulation results validate the effectiveness of the proposed algorithm.

Narrow angle approximation and isotropic assumption adopted in regular radar imaging are violated in wide angle radar imaging scenario. Therefore, conventional Fourier based methods are not directly applicable, and full aperture algorithms perform poor under low SNR. This paper proposes an imaging scheme based on Sparsity Enhanced Non-negative Matrix Factorization (SENMF). The full aperture is firstly divided into several overlapping subapertures, to which Polar Format Algorithm (PFA) is then applied to obtain subimages at different aspects. Finally, NMF with sparsity regularization is exploited to iteratively composite the full aperture image, which demonstrates enhanced target feature and improved SNR. The results of Backhoe data processing verify the validity of the novel approach.

Since inverse synthetic aperture radar (ISAR) imaging is a valuable technique in the identification of space satellites, it can potentially detect interesting components of space satellites in ISAR images to further conduct identification. This study proposes a novel method, defined as feature probabilistic estimation (FPE), to detect interesting components of space satellites based on ISAR image registration. In FPE, area feature registration is provoked to establish the relationship between space satellites and off‐line templates of interesting components, followed by detection accuracy based on weighted Gaussian probabilistic density function. Electromagnetic simulations with different aspects, interesting components' structures and scenery noise demonstrate the efficiency and robustness of the proposed FPE, compared with the normalised cross coefficient.

A multichannel terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) system is capable of imaging a wider swath with a higher azimuth resolution for improved moving target detection. For TOPS SAR, due to antenna beam steering, the azimuth bandwidth of background clutter is much larger than the instantaneous signal bandwidth. To overcome this problem, a method referred to as spectrum compression space–time adaptive processing (SC-STAP) is proposed in this letter. Through the SC process, both the Doppler spectrum and the spatial spectrum of the background clutter are simultaneously compressed. This key step achieves fully overlapped clutter space–time spectrum lines and, as such, enables effective clutter suppression and target signal alias compensation by applying linearly constrained STAP. Furthermore, in order to avoid the target ambiguities arising from the spectral wrapping, an approach based on deramp processing is proposed to focus the moving targets for TOPS SAR mode. Simulation results validate the effectiveness of the proposed algorithm.

The multibaseline phase unwrapping (PU) of L1-norm can be efficiently solved using linear programming. However, the huge memory requirement of linear programming limits its application in multibaseline PU for large-scale data. In order to reduce the required memory when linear programming is performed, a novel mixed-norm multibaseline PU algorithm is proposed in this letter, which is regarded as an approximation of the L1-norm method. In this method, an L∞-norm cost function is employed to substitute for that of the L1-norm, i.e., it takes the optimization which is aimed to minimize the maximum component of the optimization variable as the representation of the one that minimizes the absolute sum of L1-norm. Consequently, the cost function in the proposed method changes to be an L1-norm plus an L∞-norm. Compared with the traditional L1-norm method, the size of the optimization variable in the proposed method is generally reduced by about one-seventh. Therefore, it is logical that less memory is needed in the proposed algorithm. The effectiveness of the proposed algorithm is validated via a simulated and real repeat-pass interferometric-synthetic-aperture-radar data set.

This letter presents a new method of cross-range scaling in inverse synthetic aperture radar (ISAR) imaging. The effective rotational velocity (ERV), being the crucial factor for scaling, is generally unknown for noncooperative objects. By considering the degradation from target rotation, the proposed scheme estimates ERV based on image sharpness maximization. A range deviator induced by the center shift is also embedded in the estimation process. The cross-range scaling factor with an enhanced ISAR image can be obtained by an efficient Gauss-Newton method. The results acquired from both the simulations and real data experiments validate the effectiveness and robustness of the proposed method.

It is commonly known that the ionosphere has significant effects on a low-frequency (particularly P-band) radar signal. It causes the degradation of the image quality in synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging systems. In this letter, we analyze the ionospheric effects on radar signals and find that the total electron content (TEC) is a key to the ionospheric effects. A method is proposed to evaluate the TEC from a received ISAR signal and to correct the ionospheric effects. Some real experimental results, using a ground-based P-band ISAR system to observe a space target in the ionosphere, are used to validate the proposed method.

Two-dimensional phase unwrapping (PU) is a key step of synthetic aperture radar interferometry (InSAR). Moreover, the conventional single-baseline PU method is restricted to the phase continuity assumption, so it cannot work correctly in the case that phase jumps between adjacent pixels are larger than π. To effectively solve this problem, multibaseline PU is put forward. The performance of conventional multibaseline PU methods is directly related to the noise level. In order to improve noise robustness, a cluster analysis (CA) based noise-robust PU algorithm for multibaseline interferograms (CANOPUS) is proposed in this paper, which is the extension and improvement of the CA-based efficient multibaseline PU algorithm proposed by H. Yu. For the sake of overcoming the disadvantages of the CA method, the dimension of the recognizable mathematical pattern is expanded. Under this condition, due to the density discrimination in spatial space, different clusters are able to be distinguished by the density-based clustering algorithm, and clusters are regarded as a set of density-connected patterns. Compared with the conventional CA method, the significant advantage of the new algorithm is that it improves noise robustness. What is more, the proposed algorithm runs in linear time. From the experiment results, it can be seen that the proposed method may be effectively applied to multibaseline InSAR data sets.

Interferometric synthetic aperture radar (InSAR) images are corrupted by strong noise, including interferometric phase and speckle noises. In general, the scenes in homogeneous areas are characterized by continuous-variation heights and stationary backscattered coefficients, exhibiting a locally spatial stationarity. The stationarity provides a rational of sparse representation of amplitude and interferometric phase to perform noise reduction. In this paper, we develop a novel algorithm of InSAR image formation from Bayesian perspective to perform interferometric phase noise reduction and despeckling. In the scheme, the InSAR image formation is constructed via maximum a posteriori estimation, which is formulated as a sparse regularization of amplitude and interferometric phase in the wavelet domain. Furthermore, the statistics of the wavelet-transformed image is modeled as complex Laplace distribution to enforce a sparse prior. Then, multichannel imaging is realized using a modified quasi-Newton method in a sequential and iterative manner, where both the interferometric phase and speckle noises are reduced step by step. Due to the simultaneously sparse regularized reconstruction of amplitude and interferometric phase, the performance of noise reduction can be effectively improved. Then, we extend it to joint sparse constraint on multichannel data by considering the joint statistics of multichannel data. Finally, experimental results based on simulated and measured data confirm the effectiveness of the proposed algorithm.

This paper presents a new ground moving target imaging (GMTIm) algorithm for airborne synthetic aperture radar (SAR) based on a novel time-frequency representation (TFR), Lv's distribution (LVD). We first analyze generic moving target signatures for a multichannel SAR and then derive the analytical spectrum of a point target moving at a constant velocity by a polar format algorithm for SAR image formation. SAR motion deviation from a predetermined flight track is considered to facilitate airborne SAR applications. LVD, as a recently developed TFR for the analysis of multicomponent linear-frequency-modulated signal, is adopted to represent the target kinematic spectrum in the Doppler centroid frequency and chirp rate domain. As a result, the proposed SAR-GMTIm algorithm is capable of imaging multiple moving targets even when they are located at the same range resolution cell. Some practical issues such as imaging maneuvering targets and small/weak targets are discussed to enhance the applicability of the proposed algorithm. Simulation results with isotropic point moving targets are presented to validate the effectiveness and superiority of the proposed algorithm. Raw data collected by an airborne multichannel SAR are also used to verify the performance improvement made by the proposed algorithm.

This paper describes a clutter suppression approach and the corresponding moving target imaging algorithm for a multichannel in azimuth high-resolution and wide-swath (MC-HRWS) synthetic aperture radar (SAR) system. Incorporated with digital beamforming processing, MC-HRWS SAR systems are able to suppress the Doppler ambiguities to allow for HRWS SAR imaging and null the clutter directions to suppress clutter for ground moving target indication. In this paper, the degrees of freedom in azimuth for the multichannel SAR systems are employed to implement clutter suppression. First, the clutter and moving target echoes are transformed into the range compression and azimuth chirp Fourier transform frequency domain, i.e., coarse-focused images formation, when the clutter echoes are with azimuth Doppler ambiguity. Considering that moving targets are sparse in the imaging scene and that there is a difference between clutter and a moving target in the spatial domain, a series of spatial domain filters are constructed to extract moving target echoes. Then, using an extracted moving target echo, two groups of signals are formed, and slant-range velocity of a moving target can be estimated based on baseband Doppler centroid estimation algorithm and multilook cross-correlation Doppler centroid ambiguity number resolving approach. After the linear range cell migration correction and azimuth focus processing, a well-focused moving target image can be obtained. In addition, the proposed clutter suppression and imaging approach is not only adapted for uniformly displaced phase center sampling but also for the nonuniform sampling cases. Some simulation experiments are taken to demonstrate our proposed algorithms. Finally, some real measured data results are presented to validate the theoretical investigations and the proposed approaches.

Due to the effects of the earth's rotation and the satellite's elliptical orbit, the Doppler centroid varies along the orbit in geosynchronous earth orbit synthetic aperture radar (GEO SAR). With an ultrahigh orbit height, the beam may illuminate outside the earth surface with a rotation angle large than 9 degrees. Therefore, the usual attitude steering methods to minimize the Doppler centroid in low earth orbit SAR (LEO SAR) may not be suitable for GEO SAR. Considering the above two effects of GEO SAR and the beam illuminating restriction, a beam determination method to minimize Doppler centroid in GEO SAR is proposed in this paper. Guaranteeing that the beam not illuminate outside the earth surface, the proposed method can drastically decrease the Doppler centroid and the equivalent squint angle.

Airborne SAR imaging processing needs to estimate motion error to compensate non-ideal trajectory. In this paper, a subaperture imaging scheme for wide azimuth beam airborne SAR systems is proposed. First, the motion error is estimated from the subaperture data and the modified range migration algorithm is applied to obtain the coarse focused image, whose azimuth resolution corresponds to the subaperture Doppler bandwidth. The subaperture image is then projected into a fine grid image, whose coordinates is defined by the imaging geometry. As the subaperture data stream is coming, the azimuth resolution of the grid image will become higher and higher. Finally, the fine image with the azimuth resolution corresponding to the full aperture data can be obtained. Since the motion error estimation is based on the sub-aperture data, the imaging processing is suitable for real-time SAR imaging.

The amplitude‐and‐phase error (APE) between phased array channels is notorious in radar signal processing. This error can cause an inaccurate estimate of unknown steering vector of the target echo signal and eventually result in amplitude‐phase discontinuity of the phased array output. Thus, how to handle the APE is a meaningful problem, particularly for the varying jamming environment, of which the signal‐to‐noise ratio is very low. In this study, the authors have developed a new method to obtain real‐time amplitude and phase differences between two consecutive weight update periods based on the accurate estimation of steering vector. Such differences can be used to obtain a real‐time weight vector with negligible amplitude‐phase distortions, and hence improves the phased array signal‐processing performance. The proposed method is very flexible: it works well in different array configurations, such as linear, rectangular and Y‐shape arrays, and can be efficiently implemented in any eigenstructure‐based direction‐of‐arrival system.

In this letter, a novel polarimetric target decomposition (PTD) method based on the attributed scattering center (ASC) model is proposed for man-made targets in synthetic aperture radar (SAR) images. By extracting attributed parameters, polarimetric characteristics of targets can be exploited by performing PTD on the extracting parameters of ASCs instead of pixels in conventional PTD algorithms. As a result, the integrity of target components is enhanced, leading to a reliable analysis on the polarimetric scattering mechanisms of SAR targets. In the proposal, an attributed parameters extraction method based on joint exploitation of multiple polarimetric channels and a target discriminating method based on a constant-false-alarm threshold are developed to improve its robustness in strong noise scenarios. Experimental results confirm the effectiveness of the proposed algorithm.

This letter proposes a novel deramp space-time adaptive processing (Deramp-STAP) method for synthetic aperture radar (SAR) systems to achieve effective clutter suppression. Compared with the traditional STAP, the proposed method can overcome the spectral wrapping problem of a moving target from a Doppler shift. Furthermore, in the case of signal undersampling, the ambiguously focused position in azimuth for a moving target can be avoided by the proposed method. Moreover, the computational complexity can be drastically reduced because we only need to consider the baseband velocity of the moving target; here, the range of the baseband velocity is much smaller than that of the real target velocity, for clutter suppression in this method. Simulation results validate the effectiveness of the proposed algorithm.

This letter proposes a precise cross-range scaling algorithm for inverse synthetic aperture radar (ISAR) images by estimating the effective rotation angle through coordinate locations of feature points extracted from two sequenced subaperture ISAR images. In the approach, we first extract adequate feature points and feature descriptor vectors from these two images by scale-invariant feature transform and speeded-up robust features. Then, a two-stage registering scheme is employed to match these feature points to link the two images. Consequently, the effective rotation angle is efficiently and robustly estimated by evaluating a cost function based on the coordinate locations of the matched feature points. Experiments of simulated and real signals validate this proposal.

For the squinted terrain observation by progressive scans (TOPS) imaging mode, three problems need to be considered: azimuth spectrum aliasing, serious range-azimuth coupling, and azimuth time aliasing after range cell migration correction (RCMC). For these problems, a subaperture imaging algorithm based on the modified range migration algorithm (RMA) combined with spectral analysis (SPECAN) is proposed in this letter. Echo data are properly divided into subapertues so that the 2-D spectrum of each subaperture without aliasing can be obtained; then, the modified RMA is used to perform RCMC; finally, the signal is focused in the Doppler domain by SPECAN and deramping after subaperture recombination. Both simulated and real SAR data in the squinted TOPS mode are used to validate the proposed algorithm.

A space-variant chirp scaling algorithm based on the range cell migration (RCM) equalization and azimuth subband synthesis has been studied to process simulated geosynchronous synthetic aperture radar (GEO-SAR) data. The acceptable order of terms in polynomials for the slant range models in the RCM correction and phase error compensation, division of subband, and suppression of grating lobes of the subbands was investigated. Qualitatively and quantitatively, the method was able to focus simulated GEO-SAR signals well. Finally, the constraint on the spatial extent of azimuth and range dimensions using the algorithm was assessed.

The linear range walk yields a significant range-azimuth coupling effect in a highly squinted synthetic aperture radar (SAR). Although the linear range walk correction (LRWC) technique can effectively mitigate such coupling effect, it causes azimuth variation in the resulting signal and, as such, the so-called “azimuth-shift invariance” property becomes invalid. In order to eliminate the azimuth variation, a new spectrum processing approach based on azimuth resampling is proposed in this paper. After performing the LRWC, the azimuth resampling is carried out in the 2-D frequency domain and transforms the signal spectrum to be equivalent to that of a broadside SAR. For squinted beamsteering SAR (BS-SAR), e.g., spotlight SAR, sliding spotlight SAR, and Terrain Observation by Progressive Scans SAR, the azimuth resampling is combined with the azimuth signal reconstruction algorithm. As a result, both the azimuth variation, which is induced by the LRWC, and the aliasing, which is caused by antenna beam steering, can be avoided. Therefore, after the azimuth resampling, the squinted SAR data can be focused by exploiting a conventional broadside SAR imaging algorithm. An analysis of the motion error for airborne SAR data processing is also provided. Simulation and real data results show the effectiveness of the proposed algorithm.

A novel autofocus method for synthetic aperture radar (SAR) image is studied. Based on a quadratic model for the phase error within each sub-area (narrow strip × sub-aperture) after a wide range swath is subdivided into narrow range strips and long azimuth aperture into sub-apertures, an objective function for estimation of the error is derived through the principle of minimum entropy. There is only one unknown variable in the function. With the Chebyshev approximation, the function is approximated as a polynomial, and the unknown is then solved using the method of series reversion. Curve-fitting methods are applied to estimate phase error for an entire scene of the full-swath by full-aperture. Through simulations, the proposed method is applied to restore the defocused SAR imagery that is well focused. The restored and original images are almost identical qualitatively and quantitatively. Next, the method is implemented into an existing SAR data processor. Two sets of SAR raw data at X- and Ku-bands are processed and two images are formed. Well-focused and high-resolution images from plain and rugged terrain are obtained even without the use of ancillary attitude data of the airborne SAR platform. Thus, the studied method is verified.

For high-resolution wide-swath (HRWS) SAR based on multiple receive apertures in azimuth, this paper proposes a novel imaging approach for moving targets. This approach utilizes the wide bandwidth characteristics of the transmitted signal (multiple wavelengths) to estimate the moving target velocity. First, this paper explains that there is a phase mismatch (PM) between azimuth channels for the echo of a moving target, which depends on range frequency. In order to correct the PM, an algorithm based on local maximum-likelihood minimum entropy is proposed. The linear dependence of the PM on range frequency is employed to estimate the target velocity. Second, after the signal reconstruction in Doppler frequency and the compensation of the PM for a moving target, the estimated target velocity is utilized to implement the linear range cell migration correction and the Doppler centroid shifting. Then, the quadratic range cell migration is corrected by the keystone processing. After that, the focused moving target image can be obtained using the existing azimuth focusing approaches. Theoretical analysis shows that no interpolation is needed. The effectiveness of the imaging algorithm for moving targets is demonstrated via simulated and real measured ship HRWS ScanSAR data.

In order to obtain high-resolution wide-swath (HRWS) images, the multichannel in azimuth synthetic aperture radar (SAR) system has been adopted to deal with the contradiction problem between high resolution and low pulse repetition frequency (PRF). In this paper, a novel channel-calibration method is proposed for the multichannel in azimuth HRWS SAR imaging system. During the channel calibration, the mismatch between the channels, which results from the gain-phase error and the range sampling time error, is first corrected by the coarse-calibration processing in the range frequency domain. Then, the along azimuth baseline measurement error is estimated. Considering the range variance in the residual phase error, the data are processed in blocks along the range time domain, and the error of every subblock data is estimated. After that, a fitting and filtering is implemented along the range to the estimated values of the phase error of all subblocks. The range-variant phase error is then compensated using their estimated values. After channel calibration, this paper also presents a new Doppler ambiguity suppression algorithm which nulls the ambiguity components in the Doppler domain. The newly proposed algorithm outperforms the post-Doppler ambiguity suppression algorithm. The airborne real measured scan synthetic aperture radar data, which are acquired by a seven-channel in azimuth SAR imaging system with the system working at X-band, are utilized to demonstrate the performance of the newly proposed channel-calibration method and the new Doppler ambiguity suppression algorithm.

A nonlinear chirp scaling algorithm (NCSA) is proposed for bistatic SAR data processing with high squint angles in tandem configuration. Besides the dependence of the azimuth frequency, the dependence of range is considered for the Doppler chirp rate of tandem bistatic SAR. The proposed algorithm can compensate the range dependence of both the range cell migration (RCM) and the second range compression (SRC) terms well by appropriately setting the coefficients of the phase term, which is obtained after the nonlinear chirp scaling process in the two-dimensional wavenumber domain. Only fast Fourier transforms (FFTs) and phase multiplies are required, and fast imaging is implemented in the frequency domain. Satisfying focusing qualities verify the effectiveness of the proposed algorithm by simulations.

Back-projection (BP) is considered as an ideal methodology for the high-resolution synthetic aperture radar (SAR) imaging. However, applying conventional autofocus techniques to BP imagery requires a special consideration and is usually difficult to implement. In this letter, we present a scheme to compatibly blending a novel multiple aperture map drift (MAMD) algorithm with fast factorized back-projection (FFBP). Through an elaborate BP coordinate, we construct the Fourier transform relationship between FFBP sub-aperture (SA) images and the corresponding range-compressed phase history data. The phase error function is achieved by the MAMD within FFBP recursions, and well-focused imagery is obtained by phase correction on the range-compressed phase history data. The proposed scheme inherits the advantages of high precision and efficiency of the FFBP, and is suitable for high-resolution spotlight SAR imaging with raw data. Real data experiments guarantee the effectiveness of our proposed scheme.

The high-resolution imaging of a highly squinted synthetic aperture radar remains difficult because of the severe coupling between the range and the azimuth. “Squint minimization” compensates for the range walking in the azimuth time domain, which efficiently increases the orthogonality between the range and the azimuth. However, this “squint minimization” introduces the azimuth space-variant phases, which can be compensated by the azimuth nonlinear chirp-scaling (ANCS) algorithm using large computational loads. In this letter, an azimuth overlapped subaperture algorithm (AOSA) is proposed to compensate for these phases in the Doppler frequency domain. The validity constraint of this algorithm is then analyzed. The AOSA has an advantage over ANCS in terms of the computational load and is considerably more suitable for real-time processing.

In highly squinted synthetic aperture radar (SAR) motion compensation, the map-drift (MD) algorithm cannot estimate the Doppler chirp rate accurately when the range walk is compensated in the azimuthal time domain. This problem stems from the influence of the azimuthal position-dependent (i.e., space-variant) phases, which are introduced by the compensation of the range walk in the azimuthal time domain on the Doppler chirp rate estimation. This letter proposes a space-variant phase-error matching MD algorithm that can improve the precision of estimating the Doppler chirp rate for the highly squinted SAR by removing the influence of the azimuthal position-dependent phases.

Doppler rate is an important parameter in synthetic aperture radar (SAR) signal processing since it affects the SAR image focusing. There are many approaches to estimate the Doppler rate from SAR data; however, some approaches are not appropriate for spotlight SAR, which is focused with the two-step algorithm, since, after azimuth preprocessing, the signal is aliased in the azimuth time domain. Although the shift-and-correlation (SAC) algorithm may be suitable for such signals, it is proposed for the stripmap imaging mode; and when it is used to estimate the Doppler rate for spotlight SAR, some problems, such as the high computational load from zero padding and the constraint of the focus depth, may occur. In this letter, an improved Doppler rate estimation approach, which is called the Keystone-SAC algorithm, is proposed. An iterative scheme is presented to estimate the ambiguity number, and a special case when the ambiguity number splits into two numbers is analyzed. The real spotlight SAR data processing results are used to validate the effectiveness of the proposed algorithm.

When the range migration caused by motion error exceeds the range cell resolution, the performance of a conventional phase autofocus approach degrades. In this paper, a new adaptive motion compensation (MoCo) algorithm with the removal of the migration that is nonsystematic has been developed for airborne spotlight synthetic aperture radar (SAR) imagery with high resolution. In the algorithm, the relationship between nonsystematic range cell migration (NsRCM) and phase error was first explicitly revealed after the polar format algorithm resampling. The NsRCM could be readily calculated by coarse but reliable phase error estimation. Subsequently, the NsRCM and the bulk of the azimuth phase error were corrected. After the removal of the NsRCM, degradation of the conventional phase autofocus resulting from sidelobe increase as well as mainlobe broadening was avoided. Finally, a fine MoCo procedure was performed to remove the residual azimuth phase error satisfactorily. Through the analysis of the airborne spotlight SAR raw data with high-resolution and wide-swath illumination, a well-focused imagery was obtained. Quantitative assessment of the image quality was satisfactory. The MoCo algorithm was validated.

In this letter, we discuss the problem that linear range cell walk correction in the azimuth time domain may cause space variation along the azimuth not only to the quadratic phase but also to the quadratic range cell migration (QRCM) under the conditions of high resolution and large scene along the azimuth. Moreover, an algorithm is proposed to deal with this problem. The proposed algorithm adopts the azimuth space variation filtering in the range frequency domain. In addition, the range-dependence component of QRCM is corrected by linear chirp scaling, and the unified QRCM can be corrected in the 2-D frequency domain. The proposed algorithm, without interpolation, can be easily implemented by integrating with motion compensation for image processing. Simulation and airborne strip-map real data show the accuracy and efficiency of the proposed algorithm.

In airborne/stationary bistatic-synthetic-aperture-radar imaging, translational invariance was no longer valid. After range cell migration correction, the range-compressed signal under the same range gate exhibited azimuth-dependent FM rates that made the motion-induced phase error difficult to separate from the echoes. To solve this problem, an azimuth-dependent phase gradient autofocus (PGA) algorithm was proposed. Different from the conventional PGA, the residual quadratic phase arising from the azimuth-dependent FM rates was additionally estimated and compensated. As the influence of the azimuth-dependent FM rates was greatly reduced, a phase gradient estimator was subsequently applied for accurate phase error retrieval. Acquired raw data were analyzed to verify the proposed algorithm.

In the above paper (ibid., vol. 10, no, 6, pp. 1290-1294, Nov. 2013), there is an error in equation (5). The correction is presented here.

To achieve finer range resolution without increasing the bandwidth and sampling rate of the radar system, high-resolution imaging by data synthesizing using sparse subbands has received intensive attention in recent years. This paper derives the imaging geometry and signal model for radar imaging of space targets from sparse subbands. Next, it introduces and analyzes the available methods. Then, a practical method is proposed for sparse subband imaging of space targets in high-speed motion, which comprises phase compensation along the range and azimuth, gapped-data filling, scatterer number estimation, amplitude estimation, and azimuth image fusion. Finally, imaging of the simulated data using the fixed-point and electromagnetic scattering models proved the validity of the proposed method.

Range cell migration (RCM) correction and azimuth spectrum being contained entirely in baseband are critical for ground moving-target imaging (GMTIm). Without the azimuth spectrum entirely contained within baseband and a proper RCM correction, the image will be defocused, or artifacts may appear in the image. An instantaneous-range-Doppler algorithm of GMTIm based on deramp-keystone processing is proposed. The main idea is to focus all the targets in the scene at an arbitrarily chosen azimuth time. With our proposed algorithm, RCMs of all targets in the scene are removed without a priori knowledge of their accurate motion parameters. The targets with azimuth spectrum not entirely in baseband, i.e., azimuth spectrum within an ambiguous pulse repeating frequency (PRF) band or spanning neighboring PRF bands, can also be effectively dealt with simultaneously. Theoretical analysis shows that no interpolation is needed. The simulated and real data are used to validate the effectiveness of this method.

For different applications with different requirements, many synthetic aperture radar (SAR) modes have been developed in the literature, such as, Terrain Observation by Progressive Scans (TOPS) SAR and sliding spotlight SAR. In this paper, we call TOPS SAR, sliding spotlight SAR, and spotlight SAR as beam steering SAR (BS-SAR for short). Comparing with stripmap SAR, BS-SAR can obtain a wide diversity of resolutions by increasing or reducing the azimuth synthetic time. Traditional polar formation algorithm (PFA) is an efficient algorithm which is mainly developed for spotlight SAR. The PFA has been validated to obtain well-focused results of raw data. In this paper, we extend the traditional PFA to process sliding spotlight SAR and TOPS SAR data, and we call it generalized PFA (GPFA). Comparing with the traditional PFA, GPFA contains a different azimuth deramping function and an additional azimuth scaling operation. The simulated and real data are used to validate the effectiveness of this method.

Terrain Observation by Progressive Scans (TOPS) synthetic aperture radar (SAR) and spotlight SAR are advanced SAR imaging modes for wide range swath and high resolution. In order to obtain a wider range coverage, azimuth multichannel is introduced in the literature. Since the azimuth bandwidth of beam steering SAR (BS-SAR; spotlight SAR, sliding spotlight SAR, or TOPS SAR) is much greater than that of a stripmap SAR, a signal reconstruction algorithm used for multichannel stripmap SAR may not be effective for multichannel BS-SAR. In this paper, a multichannel full-aperture azimuth processing algorithm is proposed for a BS-SAR. The key of this algorithm lies in the beam and the azimuth bandwidth compressions of multichannel signals in the Doppler-array and slow time-angle planes, respectively. Through compression processing, the beamwidth and the azimuth bandwidth are smaller than the available angle and equivalent pulse repeating frequency , respectively. Then, an improved post-Doppler STAP method is proposed to recover a 2-D spectrum. With the recovered signal, further processing can be utilized to focus the multichannel signal. Simulation and real data results show the effectiveness of the proposed algorithm.

Many imaging algorithms for different modes, such as, stripmap synthetic aperture radar (SAR), spotlight SAR, sliding spotlight SAR, and terrain observation by progressive scans (TOPS) SAR, of SAR have been studied. This paper is to obtain a unified focusing algorithm (UFA) for these SAR modes based on fractional Fourier transform. By defining the rotation-center range, the stripmap SAR and spotlight SAR can be treated as special cases of sliding spotlight SAR or TOPS SAR. Then, a parameterized focusing algorithm determined by the rotation-center range is presented. Data of each mode can be focused by utilizing UFA and selecting parameters or rotation angles. Some application aspects of UFA are also analyzed. Simulation and real data results are presented to validate the analysis and the proposed method.

A novel method to obtain the formulations of the return signals in the 2-D frequency domain for both monostatic and bistatic synthetic aperture radar (SAR) is proposed. In this study, the squinted effective wavelength (SEW) is firstly used, so that the 2-D spectrums can be derived directly from their imaging geometries. For monostatic SAR (MoSAR), the 2-D spectrum is obtained without a lengthy derivation by using the widely-used principle of stationary phase. For the bistatic SAR (BiSAR), based on the assumption that the azimuth time durations of the transmitter and the receiver are the same, two individual SEWs can be derived, as well as the 2-D spectrums that are both concise and of high accuracy. Then, two modified omega-K algorithms based on the two 2-D spectrums are developed to process MoSAR and translational-invariant BiSAR data. Furthermore, as important processing steps of the proposed omega-K algorithms, a modified reference function multiplication and a modified Stolt mapping, which are much more suitable for SAR data processing than the conventional ones, are proposed. Simulations under a wide range of MoSAR and BiSAR system parameters are conducted. Finally, the proposed algorithms are applied to the analysis of acquired data and the results confirm not only the validity of the derived 2-D spectrums for both MoSAR and BiSAR but also the effectiveness of the proposed method.

For highly squinted synthetic aperture radar (SAR) imaging, the wavenumber domain SAR processing algorithm is commonly accepted as an ideal solution to SAR focusing in the case of an ideal straight sensor trajectory. However, airborne SAR is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this paper, we propose a robust autofocusing approach for highly squinted airborne SAR imagery using the extended wavenumber algorithm, being capable of estimating the range-dependent phase errors. To apply the proposed autofocusing scheme, a detailed analysis of the motion error model in the conical reference system is presented, where the formulation of range-dependent phase errors for squinted SAR is given. The proposed autofocusing approach is performed by a three-step process: referring to the inevitable residual phase after deramping for highly squinted SAR, a modified squinted phase gradient autofocusing (SPGA) algorithm is put forward to retrieve the range-independent phase errors; based on the established motion error model, the residual range-dependent phase errors are estimated using a local maximum likelihood-weighted SPGA algorithm; and motion compensation is executed by a two-step approach to reach the range-independent and range-dependent corrections, respectively. Experiments based on measured data have shown that the proposed autofocusing approach performs well for highly squinted SAR imaging.

Two-dimensional phase unwrapping (PU) is a critical processing procedure of synthetic aperture radar interferometry. Thus far, many PU methods with high accuracy have been proposed. However, the limitation of computer's memory requirement is ignored in the design of most of these methods. To effectively solve this problem, a fast PU method for large-scale interferograms is proposed in this paper. With this method, a large-scale interferogram is first partitioned into small tiles according to a strategy based on the residue clustering characteristics, which is the extension and improvement of our previous work. The new tiling strategy has a significant advantage over our earlier work, since it can exactly ensure the consistency between local and global PU results of theL1-norm criterion. In order to solve the dilemma that high execution speed and high accuracy cannot be satisfied at the same time, which is usually encountered in practice, each tile will be independently unwrapped by minimum-spanning-tree-based PU method either in parallel or in series after tiling processing. By comparing between two representative large-scale PU methods (the SNAPHU method proposed by Chen and Zebker and a large-scale minimum-cost flow method supplied by GAMMA software), it can be seen that the proposed approach is not only efficient in solving large-scale PU problems but also effective in avoiding the inconsistency between local and global PU results generated by image tiling.

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is an essential tool for modern remote sensing. To effectively deal with the contradiction problem between high-resolution and low pulse repetition frequency and obtain an HRWS SAR image, a multi-channel in azimuth SAR system has been adopted in the literature. However, the performance of the Doppler ambiguity suppression via digital beam forming processing suffers the losses from the channel mismatch. In this paper, a robust channel-calibration algorithm based on weighted minimum entropy is proposed for the multi-channel in azimuth HRWS SAR imaging. The proposed algorithm is implemented by a two-step process. 1) The timing uncertainty in each channel and most of the range-invariant channel mismatches in amplitude and phase have been corrected in the pre-processing of the coarse-compensation. 2) After the pre-processing, there is only residual range-dependent channel mismatch in phase. Then, the retrieval of the range-dependent channel mismatch in phase is achieved by a local maximum-likelihood weighted minimum entropy algorithm. The simulated multi-channel in azimuth HRWS SAR data experiment is adopted to evaluate the performance of the proposed algorithm. Then, some real measured airborne multi-channel in azimuth HRWS Scan-SAR data is used to demonstrate the effectiveness of the proposed approach.

A new way of looking at Loffeld's bistatic formula (LBF) is presented for tandem configuration in this letter. The factors that affect the precision of the spectrum are obtained through the comparison with another analytical one. It has been proved that the cosine of the half bistatic angle plays a more important role respect to the other factor, which is whether the baseline to range ratio is equal to the tangent of the half bistatic angle or not. As long as the cosine of the half bistatic angle is very close to one, the LBF spectrum is of high quality, and it does not have a direct influence by the length of the baseline (baseline-to-range ratio) or the size of the squint angle. The factors that affect the precision of the spectrum are discussed in detail through simulated experiments.

This paper proposes a novel method for high-resolution imaging of group targets moving in a formation with constant accelerated rectilinear motion. In this method, the first- and second-order phase terms are compensated simultaneously to obtain a “bulk” image of group targets. Then, regions of subtargets are determined by the segmentation method based on clustering number estimation and normalized cuts. Finally, refined compensation of the second- and third-order phase terms is carried out jointly to obtain a well-focused image of group targets, following the minimum local image entropy criterion. Simulation results have proved the validity of the proposed method.

The “stop-go” approximation is widely used for the processing of synthetic aperture radar (SAR) data, and the error brought by this assumption can be negligible for most SAR systems. However, for the SAR on a high-speed platform, with the increasing requirements on high-resolution imaging, the error may be intolerable for SAR imaging. In this case, the radar motion within a pulse repetition interval should be taken into account for the echo model and imaging algorithm. In this paper, according to the geometric configuration of the SAR working process, an accurate echo model is presented. By comparing the “stop-go” echo (which denotes the echo based on the “stop-go” approximation in this paper) with the accurate echo, the error brought by the “stop-go” approximation is introduced, and the intolerable error is shown in a reference system. A spotlight imaging algorithm based on the accurate echo is given and is well supported by the simulation results.

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is an essential tool for modern remote sensing applications. Owing to its size and weight constraints, UAV is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this paper, a novel databased motion compensation (MOCO) approach is proposed for the UAV SAR imagery. The approach is implemented by a three-step process: 1) The range-invariant motion error is estimated by the weighted phase gradient autofocus (WPGA), and the nonsystematic range cell migration function is calculated from the estimate for each subaperture SAR data; 2) the retrieval of the range-dependent phase error is executed by a local maximum-likelihood WPGA algorithm; and 3) the subaperture phase errors are coherently combined to perform the MOCO for the full-aperture data. Both simulated and real-data experiments show that the proposed approach is appropriate for highly precise imaging for UAV SAR equipped with only low-accuracy inertial navigation system.

In this letter, a new strategy based on nonnegative matrix factorization (NMF) is proposed for polarimetric ship detection. This method utilizes the sparse feature of nonnegative eigenvalues, and the sparse degree is proposed to be estimated from the histogram which can reveal the sparse distribution of eigenvalues. Combining the nonnegative and sparse features, the NMF-based ship detection method can be implemented flexibly and efficiently. It has been carried out on the C-band quad polarimetric synthetic aperture radar (PolSAR) and dual PolSAR ocean data sets to validate its effectiveness. Unlike a constant-false-alarm-rate detector, the NMF method does not depend on target size and therefore offers improved detection performance under low-signal-to-clutter-ratio conditions.

In the case of high range resolution and squint angle, current chirp scaling algorithm (CSA) and nonlinear CSA (NCSA) have a finite ability to achieve high-quality images. The problem stems from a range-dependent (i.e., space-variant) cubic- and higher order terms of range frequency, which require sufficient compensation or space-variant filtering, in the phase of the synthetic aperture radar transfer function, and this letter aims at dealing with this problem. First, an inequation is introduced to evaluate the highest order of range frequency terms whose space-variant coefficient has to be taken into account. Then, based on the method of series reversion, this letter proposes the extended NCS which can weaken the range dependence of the considered range frequency terms and achieve accurate range cell migration correction and range compression. Simulation results are presented to validate the proposed method.

During the data acquisition of a sliding spotlight or terrain observation by progressive scan (TOPS) synthetic aperture radar (SAR), the steering of the antenna main beam increases the azimuth bandwidth but could result in the azimuth signal aliasing in the Doppler domain. To remove the aliasing, one has used a subaperture method. In this letter, we show a focusing scheme without the use of the subaperture for both sliding spotlight and TOPS SARs. In doing so, we eliminated the obvious increase in data volume or the subaperture division by choosing the pulse repetition frequency that is only 20% greater than the instantaneous bandwidth. The method was incorporated with an available imaging algorithm and then used to process simulated and collected data of the sliding spotlight and TOPS SARs. Well-focused results without aliasing were obtained.

Based on compressive sampling theory, a novel method for high-resolution inverse synthetic aperture radar (ISAR) imaging is presented in this letter by transmitting sparse probing frequencies. In this method, only a few measurements in the range frequency and cross-range time domains are needed to reconstruct the target scene by solving an inverse problem through either a linear program or a greedy pursuit. By transmitting merely a few probe frequencies instead of wideband signals, the proposed method can obtain an unambiguous ISAR image with superresolution. The validity of the proposed approach is also confirmed using numerical simulations and real data.

A bistatic range migration algorithm (RMA) based on an exact analytical bistatic point-target (PT) spectrum in the tandem configuration is proposed in this letter. For the conventional geometry-based bistatic formula method, the derived spectrum is only a quasi-analytical one because a variable called half-quasi-bistatic angle (HQBA) is not exactly analytically expressed. The key step of the proposed algorithm is to deduce an analytical HQBA in the tandem configuration, and thus, an exact analytical closed-form PT spectrum is acquired. Based on this analytical spectrum, a bistatic RMA is presented. It is demonstrated that this algorithm can handle the bistatic tandem configuration with extremely large baseline/range ratio and can also be applied to wide-swath imaging.

There are two square-root terms in the range history of a return signal from a bistatic synthetic aperture radar (BiSAR). The transfer function for imaging in the 2-D frequency or range Doppler domain using the principle of stationary phase cannot be analytically derived. To address this problem, we approximated the stationary phase of the 2-D spectrum with an expansion of the Taylor series on the azimuth frequency and called the approximation as the derivatives of an implicit function (DIF). After algebraic manipulation, the DIF and 2-D spectrum were obtained for a generally configured BiSAR. With the DIF method, we dissolved one square-root term out of the two for an azimuth-invariant BiSAR, which is particularly advantageous in the implementation of an imaging algorithm. Then, a modified range Doppler algorithm was developed to process the BiSAR data. Promising results were obtained.

In this letter, a novel algorithm of inverse synthetic aperture radar (ISAR) imaging based on Bayesian estimation is proposed, wherein the ISAR imaging joint with phase adjustment is mathematically transferred into signal reconstruction via maximum a posteriori estimation. In the scheme, phase errors are treated as model errors and are overcome in the sparsity-driven optimization regardless of the formats, while data-driven estimation of the statistical parameters for both noise and target is developed, which guarantees the high precision of image generation. Meanwhile, the fast Fourier transform is utilized to implement the solution to image formation, promoting its efficiency effectively. Due to the high denoising capability of the proposed algorithm, high-quality image also could be achieved even under strong noise. The experimental results using simulated and measured data confirm the validity.

For a synthetic aperture radar (SAR) onboard a platform with a rectilinear track, the range history of a point target can be accurately expressed hyperbolically. The track can be curvilinear for a maneuverable SAR platform. The hyperbolic equation becomes inadequate, and an expression with high-order terms is needed. Using the method of series reversion, we derived the 2-D spectrum for the return signal of the curvilinear SAR. There were five independent variables in the spectrum, but available imaging algorithms could only handle three in the focusing using the spectrum. Thus, a variable-decoupling method was developed to reparameterize the initial spectrum so that only three variables remained. After the incorporation of the decoupling method into the chirp-scaling algorithm, simulations of the SAR with a curvilinear track were studied. Promising results were obtained.

Since synthetic aperture technology was employed in radar signal processing, the information capability of radar has greatly been enhanced. A lot of imaging algorithms have also been developed. However, the high-resolution imaging for highly squinted synthetic aperture radar data is still a difficult issue due to large range migration and strong range dependence on the secondary range compression term that is relatively large and cubic with high focusing sensibilities for high resolution. To accommodate for this problem, the "squint-minimization" operation and azimuth nonlinear chirp scaling (CS) (ANCS) operation are studied in this paper. On the basis of these operations, we propose new imaging algorithms and analyze the characteristic of highly squinted data and the difficulty in focusing these data as well as discussing the principle of ANCS. We also introduce a new CS algorithm, and numerical examples show that the proposed algorithm is able to achieve 0.1 m of resolution under a squint angle as large as 70°s.

From the theory of compressive sensing (CS), we know that the exact recovery of an unknown sparse signal can be achieved from limited measurements by solving a sparsity-constrained optimization problem. For inverse synthetic aperture radar (ISAR) imaging, the backscattering field of a target is usually composed of contributions by a very limited amount of strong scattering centers, the number of which is much smaller than that of pixels in the image plane. In this paper, a novel framework for ISAR imaging is proposed through sparse stepped-frequency waveforms (SSFWs). By using the framework, the measurements, only at some portions of frequency subbands, are used to reconstruct full-resolution images by exploiting sparsity. This waveform strategy greatly reduces the amount of data and acquisition time and improves the antijamming capability. A new algorithm, named the sparsity-driven High-Resolution Range Profile (HRRP) synthesizer, is presented in this paper to overcome the error phase due to motion usually degrading the HHRP synthesis. The sparsity-driven HRRP synthesizer is robust to noise. The main novelty of the proposed ISAR imaging framework is twofold: 1) dividing the motion compensation into three steps and therefore allowing for very accurate estimation and 2) both sparsity and signal-to-noise ratio are enhanced dramatically by coherent integrant in cross-range before performing HRRP synthesis. Both simulated and real measured data are used to test the robustness of the ISAR imaging framework with SSFWs. Experimental results show that the framework is capable of precise reconstruction of ISAR images and effective suppression of both phase error and noise.

The goal of this paper is to suppress the narrowband interference (NBI) and wideband interference (WBI) in synthetic aperture radar (SAR) by using a nonparametric method. The method is based on the analysis of time-frequency characteristic of NBI and WBI from which an interference suppression filter combined with the constant false alarm rate algorithm is designed. In this approach, the short-time Fourier transform (STFT) is used to estimate the instantaneous frequency of the SAR echo data with interference. In the STFT domain, the instantaneous frequency spectrum is represented by wavelet, and then, the designed filter filters the corresponding wavelet coefficients of the interference components. In addition, this algorithm is robust to time-varying NBI and WBI. The performance of the proposed approach is evaluated by the simulated and measured data, and the effectiveness is demonstrated.

In 3-D inverse synthetic aperture radar (ISAR) imaging of spinning space debris, the image obtained via the available algorithm is modified by a scaling factor. Determined by the angle between the spinning vector and the radar line of sight, this factor cannot be estimated by a monostatic radar in a short imaging interval. This letter derives the bistatic ISAR (Bi-ISAR) geometry and signal model for 3-D imaging of space debris. Then, a 3-D image scaling algorithm is introduced based on the connections between the mono- and bistatic echoes of the same scatterer. Numeric simulations have proved the validity of the proposed algorithm.

In this letter, a novel approach to autofocus for inverse synthetic aperture radar (ISAR) imaging called minimum entropy via subspace autofocus is presented. This scheme uses the weighted signal subspace to express the phase errors left in the echoes after range-bin alignment and estimates the optimal weights sequentially via an optimization algorithm based on an entropy minimization principle, and its robustness and convergence can be ensured by the optimization method. Both the theoretical analysis and processing results of the real ISAR data have confirmed the feasibility of this new scheme.

In this letter, a novel single-range image fusion method for radar imaging of spinning space debris with dimensions smaller than the radar range resolution is proposed. On the assumption that the target consists of isolated isotropic scattering centers, a 2-D image can be obtained using single-range unit cross-range echo data, which have a theoretical resolution of a quarter of a wavelength. The proposed approach is computationally efficient particularly for smaller sized targets, as the total rotational angle is divided into four small sections, allowing for easier processing of each region. Moreover, the proposed method can be used to directly obtain an image in Cartesian coordinates, unlike current algorithms where images are obtained in a polar format, requiring reformatting to the Cartesian grid. The validity of the proposed approach is confirmed using numerical simulations.

A novel approach to motion parameter estimation with low pulse repetition frequency (PRF) sampling based on compressed sensing (CS) theory is introduced. As is known to us, when PRF is less than the Doppler spectrum bandwidth, moving targets suffer both Doppler centroid frequency ambiguity and Doppler spectrum ambiguity. Under this condition, the traditional parameter estimation method in the Doppler domain is out of action. The key of this letter converts motion parameter estimation in the synthetic aperture radar system with low PRF sampling into solving an optimization equation based on CS theory. Because moving targets in the scene can be regarded as sparse signals after clutter cancellation, an optimization algorithm based on CS theory is proposed to reconstruct sparse signals and meanwhile estimate the along-track velocities and azimuth positions of moving targets. Considering the fact that range cell migration of moving targets is not subject to PRF limitations, Radon transform is adopted to obtain unambiguous across-track velocities and range positions. Results on simulation and real data are provided to show the effectiveness of this method.

The theory of compressed sampling (CS) indicates that exact recovery of an unknown sparse signal can be achieved from very limited samples. For inversed synthetic aperture radar (ISAR), the image of a target is usually constructed by strong scattering centers whose number is much smaller than that of pixels of an image plane. This sparsity of the ISAR signal intrinsically paves a way to apply CS to the reconstruction of high-resolution ISAR imagery. CS-based high-resolution ISAR imaging with limited pulses is developed, and it performs well in the case of high signal-to-noise ratios. However, strong noise and clutter are usually inevitable in radar imaging, which challenges current high-resolution imaging approaches based on parametric modeling, including the CS-based approach. In this paper, we present an improved version of CS-based high-resolution imaging to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. Real data are used to test the robustness of the improved CS imaging compared with other current techniques. Experimental results show that the approach is capable of precise estimation of scattering centers and effective suppression of noise.

A new inverse synthetic aperture radar (ISAR) imaging approach is presented for application in situations where the maneuverability of noncooperative target is not too severe and the Doppler variation of subechoes from scatterers can be approximated as a first-order polynomial. The proposed algorithm is referred to as the range centroid Doppler (RCD) ISAR imaging technique and is based on the stretch Keystone-Wigner transform (SKWT). The SKWT introduces a stretch weight factor containing a range of chirp rate into the autocorrelation function of each cross-range profile and uses a 1-D interpolation of the phase history which we call stretch keystone formatting. The processing simultaneously eliminates the effects of linear frequency migration for all signal components regardless of their unknown chirp rate in time-frequency plane, but not for the noise or for the cross terms. By utilizing this novel technique, clear ISAR imaging can be achieved for maneuvering targets without an exhaustive search procedure for the motion parameters. Performance comparison is carried out to evaluate the improvement of the RCD technique versus other methods such as the conventional range Doppler (RD) technique, the range instantaneous Doppler (RID) technique, and adaptive joint time-frequency (AJTF) technique. Examples provided demonstrate the effectiveness of the RCD technique with both simulated and experimental ISAR data.

This paper discusses parallel bistatic synthetic aperture radar (SAR) processing using chirp scaling algorithm. The key step is to use an analytical form of the signal spectrum derived by the geometry-based bistatic formula (GBF) method. With the above formula, a chirp scaling (CS) algorithm is proposed for azimuth-shift-invariant bistatic SAR processing. The presented algorithm can well resolve the range variation of motion through range cell(MTRC) for bistatic SAR, and requires no interpolate; it requires only FFTs and complex multiplies, these attributes lead to efficient implementations of FFT-based signal processors and high speed parallel processors; it can be used for high resolution image formation.

In this letter, a 3-D inversed synthetic aperture radar imaging algorithm for targets in high-speed spinning is proposed based on 2-D spectrum matched filter (MF) banks. Each spectrum MF bank yields a focused slice for its corresponding scatterers. By extracting the spatial parameters from all slices, the 3-D image of the target can be constructed. Numeric simulation confirms the validity of the algorithm.

Recent theory of compressed sampling (CS) suggests that exact recovery of an unknown sparse signal with overwhelming probability can be achieved from very limited number of samples. In this letter, we adapt this idea and present a framework of high-resolution inverse synthetic aperture radar imaging with limited measured data. During the framework, we mathematically convert the imaging into a problem of signal reconstruction with orthogonal basis; hence, a conceptive upper bound of the cross-range resolution is presented based on the CS theory. Real data results show that the CS imaging approach outperforms the conventional range-Doppler one in resolution.

Azimuth ambiguity occurs in synthetic aperture radar (SAR) systems due to the well-known constraint of minimum antenna area, particularly at high resolutions and wide swaths. A space time domain method can be utilized to remove this ambiguity if the multiple-channel data are available. In this letter, a modified approach is presented to determine the filter weight vectors. This approach was successfully applied to the real data, which were collected by an experimental airborne multiple-channel SAR system. The channel imbalance and the error in antenna phase center position are analyzed in detail.

Narrow-band interference (NBI) is a common interference source in synthetic aperture radar (SAR) imaging. Its existence will degrade the imaging quality greatly. Based on detailed analysis on the characteristics of NBI, this letter proposes a new NBI suppression algorithm using the complex empirical mode decomposition (CEMD) method. In this algorithm, echoes that include NBI are recognized in the time domain first. Then, these echoes are decomposed into a number of intrinsic mode functions (IMFs) via the CEMD. After that, IMFs that correspond to NBI are subtracted from the echoes by thresholding. Finally, well-focused SAR imagery can be obtained from the separated target echoes using traditional SAR imaging algorithms. The effective data loss in this algorithm is smaller than other NBI suppression approaches. In addition, this algorithm is robust to time-varying NBI. Imaging results of measured data have proved the validity of this algorithm.

Since space debris could post a significant threat to orbiting objects around the Earth, their reorganization, measurement, and catalogue are of great importance. This paper establishes a 3-D inverse synthetic aperture radar (ISAR) imaging geometry and signal model for space debris. Then, a 3-D imaging algorithm is proposed to realize coherent imaging in the range-slow-time domain. This algorithm is based on the complex-valued back-projection transform according to the spinning nature of space debris. The simulation results for both point scattering and continuous targets have proved the validity of the proposed algorithm.

Ground moving target indication (GMTI) is one of the most important applications in a general bistatic synthetic aperture radar (SAR) system, where the transmitter and receiver move along nonparallel trajectories with different velocities. In order to improve the capability of clutter cancellation in bistatic SAR/GMTI processing, the coherence between two echoes collected by two receivers is investigated, and the full-coherence conditions are derived. A new coherence-improving algorithm for general bistatic SAR complex image pairs is proposed, which can be realized in the following steps: 2-D range azimuth prefiltering processing, relative geometric deformation correction, and image registration. An approximate implementation of 2-D prefiltering and the corresponding prefilter parameter analysis are also given. Last, two numerical experiment results are given to demonstrate the effectiveness of the proposed algorithm.

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is very important for battlefield awareness. For SAR systems mounted on a UAV, the motion errors can be considerably high due to atmospheric turbulence and aircraft properties, such as its small size, which makes motion compensation (MOCO) in UAV SAR more urgent than other SAR systems. In this paper, based on 3-D motion error analysis, a novel 3-D MOCO method is proposed. The main idea is to extract necessary motion parameters, i.e., forward velocity and displacement in line-of-sight direction, from radar raw data, based on an instantaneous Doppler rate estimate. Experimental results show that the proposed method is suitable for low- or medium-altitude UAV SAR systems equipped with a low-accuracy inertial navigation system.

For rapidly spinning targets, such as the rotating ground radar antenna, helicopter blades, spinning space debris, etc., the scatterers on the target may rotate for several periods in the observation time. Since the range and Doppler information of these scatterers are no longer constant, the conventional range-Doppler-based imaging algorithms are invalid. Meanwhile, 3-D imaging is necessary to obtain additional information for the spinning target. However, the available interferometric inverse synthetic radar (ISAR) and snapshot 3-D imaging algorithms do not work well since they require low target spinning speed. In this paper, a matched-filter-bank-based 3-D imaging algorithm for rapidly spinning targets is proposed, based on target motion features. This algorithm utilizes the rapidly rotating turntable model of the ISAR target instead of the slow rotating one. First, 2-D image slices of the target are obtained from the output of the matched filter bank by changing matching parameters. Then, a series of 2-D image slices are combined to form the 3-D target image. Since this algorithm applies to the monostatic radar system, it is easy to implement in practical applications. Both the theoretical derivation and the simulation results have proved the validity of the proposed algorithm.

The bistatic synthetic aperture radar (SAR) data, which are converted into equivalent monostatic data by proper preprocessing, can be processed by standard monostatic focusing algorithms. The dip moveout (DMO) approach, which is derived from seismic data processing, converts the bistatic data into equivalent monostatic data by a short time-domain Rocca's smile operator. A 2D exact point-target (PT) reference spectrum is derived in this letter for the tandem bistatic configuration. The geometry-based bistatic formulation is shown to be actually equivalent to Rocca's smile operator, although they are derived from the pure SAR and geophysics points of view, respectively. Moreover, the new PT spectrum can be extended to deal with azimuth-invariant bistatic SAR data. Interpretations on the equivalent monostatic range wavenumber are presented in this letter, which help understand the conversion from the radar signal processing viewpoint.

This letter derives the bistatic point target spectrum for the translational invariant case. Based on the derived spectrum and the linear approximation of the bistatic range cell migration factor, a bistatic frequency-scaling algorithm (FSA) is proposed for spotlight synthetic aperture radar data processing. This algorithm leads to a very precise and efficient processing since it performs the focusing in the frequency domain and no interpolation is required in the whole processing chain. In addition, it is shown that the case considered in the monostatic FSA is a specialization of the more general one considered in this letter. Finally, simulation results are provided to illustrate the validity of the presented approach.

Spotlight mode offers finer azimuth resolution than that achievable in stripmap mode using the same physical antenna. The most popular spotlight synthetic aperture radar (SAR) reconstruction method is the classic polar format algorithm (PFA). However, PFA needs to perform range and azimuth interpolation operations during SAR imaging, which, in turn, affect the imaging precision and the computation efficiency. In this letter, we present a novel polar format algorithm, which may avoid interpolation operations by double chirp-Z transform. In this letter, we also analyze the nondefocus and the nondistortion constraints and discuss the along-track acceleration constraint for the PFA as well.

For micromotion targets with rotating parts, the inverse synthetic-aperture-radar image of the main body may be shadowed by the micro-Doppler. To solve this problem, this paper proposes an imaging algorithm based on the complex-valued empirical-mode decomposition. First, the radar echoes are decomposed into a series of complex-valued intrinsic-mode functions (IMFs). Then, the IMFs from the rotating parts and those from the main body are separated according to the characteristics of their zero-crossings. Finally, the well-focused imaging of the main body via traditional imaging algorithm and the accurate parameter estimation of the rotating part can be obtained. Both the imaging results for the simulated and measured data are given to verify the validity of the proposed algorithm.

A 3-D inverse synthetic aperture radar imaging method for rapidly spinning targets, i.e., a generalized Radon transform (GRT)-CLEAN algorithm, is proposed in this paper. The signal model is first changed into an equivalent high-speed turntable model after the compensation of the translational motion. Second, based on the relationship between the range profile variation of the spinning targets and the scatterers' positions, the GRT is utilized to estimate the scatterers' positions. Finally, combining the GRT with the modified CLEAN approach, the parameters of each scatterer and, thus, the 3-D image of the targets can be obtained. In addition to the development of the GRT-CLEAN algorithm, the estimation and compensation of the linear translational motion error in the imaging process is also considered in this paper. Good images yielded confirm the effectiveness of the GRT-CLEAN algorithm in the simulations.

A new algorithm for filling sparse aperture synthetic aperture radar (SAR)/inverse SAR (ISAR) data, which applies for widely gapped apertures, is proposed in this letter. An Estimating Signal Parameter via Rotational Invariance Techniques(ESPRIT)-based parametric approach is first used to estimate the power distribution with the sparse data. With the estimated power spectrum as prior information, by minimizing a weighted norm as a constraint, the full aperture data can be estimated. Although the algorithm is proposed for the sparse aperture interpolation in SAR/ISAR, it can be applied to other gapped data spectral estimation problems as well. Both numerical and experimental examples are provided to demonstrate the performance of the proposed algorithm.

In the recent literature, only single-range Doppler interferometry (SRDI) has been considered for radar imaging of space debris with dimensions smaller than the radar range resolution. Considering the typical trajectories and dynamics of space debris, a single-range matching filtering (SDMF) approach is proposed in this letter. SDMF permits obtaining a 2-D image of space debris by using range unit cross-range echo data and by matched filtering the signals with different turning radii. The analysis and simulations show that, compared with SRDI, the proposed approach has less computational load and better images, particularly, for targets with smaller size.

Synthetic aperture radar (SAR) has found wide applications in many areas, e.g., battlefield awareness. However, SAR is vulnerable to various kinds of interference, among which narrow-band interference (NBI) is commonly used. In this letter, an eigensubspace-based filtering approach is proposed for NBI suppression in SAR without using passive-sniff data as the reference signal. Moreover, the proposed method can deal with smart or interrupted NBI. Both simulation and experimental results are provided to illustrate the performance of the proposed approach

In recent years, bistatic synthetic aperture radar (BiSAR) has attracted the attention of many radar researchers. It is well known that the slant range history of BiSAR is the sum of two square-rooted terms, which correspond to the transmitting and receiving slant ranges, respectively. For a point target in the SAR scene, it is quite difficult, if not impossible, to obtain an analytic formula to describe the target echo data in the 2-D frequency domain without any approximation by using the conventional stationary phase method, which makes it very difficult to develop fast-focusing algorithms for BiSAR. In this paper, based on the concept of an instantaneous Doppler wavenumber and by defining a new variable called half quasi-bistatic angle, an analytic formula of the point target response in the spectral domain is developed for BiSAR with parallel trajectory (referred to as parallel BiSAR for simplicity). Relying on a first-order Taylor expansion of the above formula with respect to the parameter called the sum of closest distances on the swath center, a bistatic range migration algorithm is proposed for any azimuth-shift-invariant BiSAR data processing. Simulation results have confirmed the effectiveness of the proposed novel approach.

In this paper, a new adaptive filtering algorithm is proposed to remove speckle in SAR images. This method differentiates the detail edge information from the noisy images based on scale space correlation. The Wiener filter is used to deal with the edge information of points and the Bayesian soft threshold is applied to the noisy points in homogeneous areas to reduce speckle. After processed with this algorithm, SAR images can not only achieve a satisfactory despeckling effect but also show good performance in preserving details and texture information.

Range-Doppler (RD) processing is widely used in conventional inverse synthetic aperture radar (ISAR) imaging. The unwanted translational motion of moving targets is compensated by envelope alignment and autofocus. For existing ISAR imaging algorithms, the scatterers' migration through resolution cells (MTRC) caused by the rotational motion is usually ignored. With the improvement of resolution or the increase of target size, MTRC cannot be neglected. In this letter, the keystone formatting algorithm developed in SAR is used for the MTRC compensation in ISAR. Before the keystone formatting, coherent processing must be performed on the raw phase history data. An effective approach is proposed for this kind of coherent processing. Numerical examples are provided to demonstrate the performance of the proposed approach.