Video Synthetic Aperture Radar enables high-resolution, continuous imaging of observed scenes under all-weather and day-night conditions. Nevertheless, video SAR image reconstruction remains challenged by substantial data volumes and high computational complexity. This study addresses these limitations by exploiting temporal redundancy in sequential frame data. Through systematic analysis of video SAR data characteristics, we formulate video SAR imaging as a sparse tensor recovery problem by introducing a tailored correlation function to leverage inter-frame dependencies. An iterative solution is derived by integrating the alternating direction method of multipliers (ADMM) and proximal-alternating inexact minimization (P-AIM) frameworks. Based on this formulation, we propose an imaging network (ViSAR-UTNet) by unfolding the iterative process into a Transformer architecture. ViSAR-UTNet comprises two core modules: a weighted self-attention (WSA) mechanism that learns inter-frame correlations and a linearized ADMM (LADMM) operator for sparse tensor recovery. By leveraging the unfolded Transformer structure, ViSAR-UTNet effectively exploits data redundancy, thereby enabling high-quality video reconstruction from reduced measurements. Experiments on synthetic and real datasets are conducted to validate ViSAR-UTNet. The results demonstrate enhanced reconstruction accuracy and computational efficiency of the proposed method.

With multiple channels receiving echoes in azimuth, multichannel SAR has the potential of forward-looking imaging. However, its angular resolution, especially the area along the platform flight path, is poor due to the small variation of viewing angle. In this paper, the scheme of angular resolution enhancement for multichannel forward-looking SAR imaging based on zero-shot learning is proposed. In the scheme, the preliminary imaging results are obtained firstly with the synthetic aperture processing, and then the results are divided into three equal parts in azimuth, namely left, middle and right parts, to construct the dataset. Then by incorporating the frequency domain loss and mixed attention module, the modified CycleGAN network is developed to learning the mapping between the middle part (the area with lower resolution) and the left/right parts (the area with higher resolution). At last, experimental results are illustrated to verify that the proposed scheme can enhance the angular resolution of the middle area to the level of left/right area for the forward-looking images even without ground truths.

Due to the high degree of freedom of bistatic synthetic aperture radar (SAR), how to design a suitable configuration to achieve forward-looking high-quality imaging is a crucial issue. Since the spatial resolution is the most critical performance metric, the configuration design of bistatic forward-looking SAR (BFSAR) driven by spatial resolution metrics is discussed in this letter. First, based on the general geometry configuration and generalized ambiguity function (GAF), the analytical expression of the ellipse resolution for BFSAR is derived. Then, a novel measure for spatial resolution is developed by considering both the orthogonality and balance. Finally, with the prior information of the transmitting platform, the optimal configuration of the forward-looking receiver is established by solving an optimization problem of the spatial resolution with genetic algorithm, and simulation results are illustrated to validate the effectiveness of the proposed method.

The reconstruction of the three-dimensional structure of synthetic aperture radar (SAR) targets is a hot and difficult issue in the field of SAR. Conventional 3-D reconstruction methods based on 2-D SAR images do not consider the inherent characteristics of SAR imaging such as geometric deformation, overlap, and occlusion, and can only reconstruct simple and regular targets. To address this, we propose a CNN-based SAR 3-D reconstruction method called Multi-aspect SAR Stereo Network (MASS-Net). Our network is an end-to-end deep learning architecture that can automatically complete dense matching among multi-aspect SAR images and calculate the height to obtain height maps by learning prior knowledge. In the network, a feature extractor based on CNN is constructed to extract features from SAR images, which can extract high-dimensional features of 2-D SAR images, and help to capture neighborhood information and overcome the influence of geometric deformation and occlusion. Then a differentiable SAR projection relationship is established to construct a cost volume that includes the differences in multi-aspect image features. This projection relationship ensures ensures the overall differentiability of our pipeline. Meanwhile, the encoding and decoding architecture based on 3-D CNN is utilized to achieve regularization and regression to generate height maps. Finally, we use multi-aspect height maps to construct a dense 3D point cloud of the target. These make MASS-Net efficient and effective. Compared with traditional methods, our method can address issues such as distortion and occlusion, and efficiently reconstruct dense and accurate 3-D point cloud of complex targets. Simulation experiments and actual measurement experiments have been conducted to verify our proposed method.

Multichannel synthetic aperture radar (SAR) has the potential of resolving left/right ambiguity and then achieving high-resolution forward-looking imaging. However, when the sparse channel configuration is adopted, there are always residual grating lobes and left/right ambiguity in the imaging results. In this paper, a scheme of simultaneously suppressing residual grating lobes and left/right ambiguity for sparse channel forward-looking SAR imaging is proposed. Firstly, the formation of the residual grating lobes and left/right ambiguity are analyzed based on the signal model and imaging procedure of multichannel forward-looking SAR. Then, the characteristics of the differences in the position of grating lobes for the imaging results of different snapshots, and the spatiotemporal coupling characteristics of suppression of grating lobes and left/right ambiguity are explained. Next, a space-time steering matrix based on range history information is constructed, which establishes a linear mapping relationship between the space-time echo and the original scene. By solving the linear equation with a regularized iterative adaptive approach, the residual grating lobes and left/right ambiguity are suppressed simultaneously. The extensive simulation and experimental results demonstrate the effectiveness of the proposed method.

With the increasing number of electromagnetic devices, radio frequency interference (RFI) suppression has gradually become an essential problem in synthetic aperture radar (SAR) imaging. Faced with complex RFI environments such as time-varying and multitype mixing that may occur, traditional approaches often result in inadequate suppression and loss of valuable echoes. Moreover, the representation ability of manually extracted features is limited, struggling to maintain consistent performance in complex electromagnetic environments. To tackle these challenges, this article proposes a feature-enhanced low-rank and sparse decomposition network (FELS-Net), which separates RFI and useful echoes in the time-frequency domain (TFD). We introduce two learnable invertible nonlinear transforms to enhance the representation of RFI and SAR echoes, and unfold the RFI suppression scheme based on low-rank and sparse decomposition into a parameter-learnable network structure. The strong interpretability of model-driven architecture offers a potential stability guarantee for RFI suppression performance, while deep learning (DL) contributes to more effective feature characterization and more efficient and robust parameter schemes. Experimental results demonstrate that the proposed method outperforms comparative approaches in both time-frequency (TF) and image domains, exhibiting robust performance across diverse experimental conditions.

Bistatic Synthetic Aperture Radar (BiSAR) enables highly flexible configuration, offering broad application prospects. However, existing BiSAR imaging algorithms neglect the complex scattering characteristics of the target, resulting in the loss of target structural information in the imaging results. To enhance the target structural information in imaging results and improve the interpretability of BiSAR images, we first establish the BiSAR echo model based on the target scattering model and analyze the target’s imaging characteristics by incorporating the imaging mechanisms. Subsequently, based on the imaging characteristics, we propose a structure-driven multi-stage BiSAR trajectory planning method (SMTP). This method solves a multi-stage multi-objective optimization problem driven by target scattering characteristics, thereby fully presenting all discernible structural features in the imaging results. Numerical simulation experiments validate the proposed method, demonstrating its ability to recover target structural information. This approach addresses the gap where BiSAR mission planning has largely overlooked target-specific characteristics.

Range sidelobe is a common and widely concerned issue in synthetic aperture radar (SAR) imaging. The sidelobe of strong scatters could cover weak targets and reduce the quality of SAR images, increasing the difficulty in interpretation. Due to the principle of energy conservation, reducing the sidelobe will reduce the range resolution of SAR for the single waveform design. By taking full advantage of the degrees of freedom of the transmitter, transmitting multiple waveforms, and exploiting the information discrepancy between pulses, we can overcome this limitation. Complementary sequences are a typical example since the range sidelobe of the sequences can be theoretically summed up to zero. Due to the transmission of different waveforms between pulses, an amplitude-phase modulation will be introduced in the azimuth dimension of SAR echo, which, in turn, leads to the Doppler spectrum aliasing and unexpected energy spikes outside the main energy region of SAR point spread function (PSF). Seeking to suppress the range sidelobe and meanwhile mitigate the spikes caused by waveform agility in SAR, we first establish the expression of the echo under the agile transmitting mode. Then, we propose an SAR PSF shaping (SAR-PSFS) method to suppress the integrated sidelobe level (ISL) of the whole range sidelobe plane in the SAR image via complementary waveform optimization. The inexact alternating direction penalty method (IADPM) framework is adopted to solve the resulting nonconvex optimization problem. Simulation results show that the proposed method outperforms the nonlinear frequency-modulation (NLFM) signal with a 5.46-dB lower integrated sidelobe ratio (ISLR) under the same mainlobe width. Besides, the range sidelobe can be effectively canceled with a 3.26-dB lower peak sidelobe ratio (PSLR) and a 4.99-dB lower ISLR compared with the Hanning window while maintaining the range resolution with the spikes caused by waveform agility effectively mitigated.

Multistatic three-dimensional synthetic aperture radar (3D SAR) has shown significant potential in rapid 3D imaging. Compared to traditional multi-pass or array 3D imaging systems, it achieves high-resolution imaging in a single pass. However, due to the introduction of multiple radar systems, decoherence factors such as multi-channel and synchronization cause serious degradation in data quality, posing challenges for accurate reconstruction. To address this issue, this paper proposes a data recovery algorithm based on matrix completion (MC) for 3D imaging of structured targets. The structural characteristics of architectural targets introduce a low-rank property into the data, stemming from the inherent correlation among adjacent pixels. Utilizing the principle of matrix completion, combined with the sparsity of scattering points in elevation, a low-rank and sparse joint completion model is established. Furthermore, the Truncated Schatten-p Norm and Sparse Regularizer-Alternating Direction Method of Multipliers (TSPN-ADMM) algorithm is adopted for solving. Additionally, considering that this recovery method reconstructs the 2D complex image, a Filter-MC processing framework is proposed to further enhance the performance. Finally, both simulation and real data verify the effectiveness of the proposed recovery method and framework.

Obtaining the clear contours of ship targets via Synthetic aperture radar (SAR) is extremely valuable for monitoring the sea. Now there are many deep learning imaging methods for ground scenes with good results, but they will face three main challenges when imaging ship targets: 1) The translational and rotational motions of ship targets during travel and due to waves respectively bring spatial invariant and variant errors that are tough to be estimated and compensated, resulting in defocused SAR imaging results; 2) The varying motion of ships demands high generalization ability of the imaging reconstruction network to adapt to the ship targets with sample-wise variant motion parameters; 3) Since ships are noncooperative targets, the accuracy of motion parameter estimation should be evaluated based on image quality, whereas the available SAR image quality assessment functions exhibit limited robustness. To address these issues, this article proposes a deep learning-based SAR imaging framework for ship targets via deep unfolding. Firstly, the motion model and characteristics of ship targets are analyzed, and the SAR imaging model for ship target with complex translational and rotational motion is established. Secondly, an imaging network with high generalization ability is proposed to adapt echoes for imaging under different motion parameters. On this basis, a SAR ship image quality assessment network is proposed to assess the imaging results of SAR ship targets with different focusing qualities. Then, the high-resolution imaging problem of ship targets is regarded as a motion parameter optimization problem, with the image quality assessment results as the objective function. Finally, this problem is optimized to search for the most accurate translational and rotational motion parameter variables of the ship target, achieving error compensation and imaging. The validity of the method is verified though the simulation of point targets and real SAR scenario data.

With multiple channels in azimuth receiving echoes, multichannel radar has the potential of forward-looking imaging, and various schemes can be formulated. However, due to the different resources utilized by different imaging schemes, the angular resolution will be different. How to analyze the angular resolution and then design appropriate parameters is a key issue in multichannel radar forward-looking imaging. In this paper, based on the echo model of forward-looking imaging, the imaging schemes of synthetic aperture and real aperture are illustrated firstly. Then, based on the ambiguity function theory, the angular ambiguity functions, and the analytical expressions of the angular resolution for different imaging schemes are derived and analyzed. Finally, the simulation results of point targets and extended targets are presented to verify the effectiveness of the theoretical analysis, which would lay a significant foundation for the design of forward-looking imaging schemes of multichannel radar.

Distributed synthetic aperture radar (SAR) tomography has recently aroused extensive attention due to its 3-D imaging capability and flexibility. However, atmospheric turbulence brings in motion errors, and collaborative work of multiple platforms increases the degree of freedom (DoF) for motion errors and introduces time-frequency synchronization errors. These undesired errors result in severe 2-D image defocusing, making SAR tomography 3-D imaging unattainable. To address these issues, a novel 3-D focusing scheme that can simultaneously compensate for high DoF motion errors and time-frequency synchronization errors is proposed. Firstly, the RCM offset induced by time synchronization errors is corrected through preprocessing. Then, image-quality-based 2-D autofocus is utilized to achieve 2-D focusing. Finally, the effects of linear phase and constant phase introduced by 2-D autofocus are eliminated by image registration and elevation autofocus based on maximizing image sharpness, respectively. Simulation results demonstrate the effectiveness of the proposed 3-D focusing scheme.

Microwave Photonic Synthetic Aperture Radar (MWP-SAR) holds significant promise for applications in Earth remote sensing owing to its exceptional imaging resolution. This radar technology emits ultra-wideband signals surpassing those of conventional radar systems. Hence, MWP-SAR exhibits the potential to generate pseudo-color images by exploiting scattering differences, thereby augmenting the information acquisition capabilities of MWP-SAR. This paper introduces a methodology for synthesizing pseudo-color images while preserving the high resolution of MWP-SAR. The proposed algorithm employs an optimization technique to identify subband echo channels exhibiting the most significant differences in scattering characteristics. Simultaneously, to safeguard the resolution of MWP-SAR, a fusion model is devised to integrate the full-resolution Synthetic Aperture Radar (SAR) image with the multi-subband image. Finally, a full-resolution pseudo-color image was successfully synthesized from the measured airborne MWP-SAR data.

Sea clutter suppression plays a crucial role in maritime moving target indication. However, in the bistatic SAR (BiSAR) system, traditional space-time adaptive processing (STAP) method can’t satisfy the expected performance due to severe range cell migration (RCM), Doppler frequency migration (DFM), nonstationary clutter, and spatio-temporal spectrum expansion caused by the internal motion of sea clutter. To issue these problems, a matrix sparse model based spatio-temporal spectrum recovery method is proposed. The proposed method mainly consists of three steps. Firstly, Generalized Keystone transform in preprocessing stage is used for RCM correction and DFM compensation. Then, multiple spatio-temporal samples acquisition strategy for cell under test is designed, to enhance the solution robustness. Next, a matrix sparse model for BiSAR spatio-temporal spectrum recovery is constructed and solved. Finally, the sea clutter suppression performance is verified with numerical simulations.

Synthetic aperture radar (SAR) usually uses linear frequency modulation (LFM) signal to image the target and clutter signal, which often causes the target to be overwhelmed by strong clutter. To solve this problem, we propose a waveform design method which can reduce the clutter and guarantee the SAR imaging performance. Firstly, the spectrum mask constraints of signal-to-clutter ratio (SCR) are obtained according to the scene prior information, and then the unimodular waveform of minimizing weighted integral sidelobe level (WISL) is designed. The final result is the SAR transmit waveform with spectral constraints. Simulation results show that the design can effectively achieve target enhancement and clutter suppression while maintaining SAR imaging performance.

High-resolution ship Inverse Synthetic Aperture Radar (ISAR) imaging is vital for identifying ship targets by revealing their contours and detailed features. However, the spatial-variant Doppler frequency caused by unknown motion parameters often leads to defocused ISAR images. Traditional Fourier-Transform-based methods struggle to handle the three-dimensional spatial-variant Doppler frequency (3-D SVDF). CLEAN-based methods, capable of estimating 3-D SVDF, often sacrifice target details. To overcome these hurdles, this paper proposes a refined ship ISAR imaging method based on Fractional Fourier Transform (FRFT). Employing FRFT, the method estimates multiple Doppler parameters and segregates scattering points into spatial-invariant sub-images based on their Doppler similarity. Phase compensation is then applied to each sub-image, enabling compensation for ship motion and extracting detailed ship target information in high-resolution ISAR images. Simulation results affirm the algorithm's efficiency in achieving image focusing and revealing intricate ship target details.

In the field of polarimetric synthetic aperture radar (PolSAR) automatic target classification (ATR), convolutional neural network (CNN) based methods have excelled owing to their adept feature extraction capabilities. However, these methods heavily rely on a sufficiently labeled training dataset for superior classification performance. Limited PolSAR training samples and inevitable noisy labels often render CNNs susceptible to overfitting. To tackle this challenge, a novel PolSAR image classification method employing beta mixture model and boundary amplification is proposed. Initially, the beta mixture model is utilized to fit the loss value distributions of noisy and clean samples, enabling the exploitation of distinct characteristics between these samples for probability estimation. Subsequently, to emphasize boundary samples, the boundary is delineated and expanded using the Sobel operator, amplifying losses for samples within this expanded region. Finally, a robust classification loss function is integrated into the training process to rectify losses incurred by network predictions. Experimental validation conducted on the Flevoland dataset demonstrates that the proposed method attains state-of-the-art performance.

Ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental step in the maritime surveillance. However, recognition of ships inevitably faces background interference in the maritime environment. The interference guides the network focusing on useless even harmful regions. To deal with issue, a mixed attention mechanism consists of coordinate and Squeeze-and-Excitation(SE) attentions is introduced. The mixed attention can guide the network to focus more on the target region, decreasing the influence of useless interference regions. Experimental and visualize results on benchmark dataset OpenSARShip validate the effectiveness of our idea.

Space-borne passive radar (PR) exploits non-cooperative signals of satellite constellations, specifically Low-Earth-Orbit (LEO) communication satellites. It offers advantages such as low intercept probability and reduced costs. However, utilizing LEO signals presents challenges as multiple similar signals from different satellites can interfere without identification. This paper proposes a method for LEO communication signal separation and target detection leveraging synchronization signal blocks (SSB) for discrimination and identification, generating a reference signal for subsequent processing to transfer target information to the range-Doppler domain. The final integration result enables the detection and localization of the target. The effectiveness of the proposed method is validated through simulated experimental results.

With the launch of high-resolution (HR) synthetic aperture radar (SAR) imaging satellites and the rapid development of convolutional neural networks (CNNs), ship recognition in HR SAR images has shown further improvements. Unlike ship targets in low and medium-resolution SAR images, which only possess a few pixels and present a spot-like appearance, ship targets in HR SAR images pose a larger area of pixels. However, CNN lacks the power to model dependencies between long-range features occupying large areas of pixels. A convolutional transformer (CvT) is introduced to deal with this issue. CvT integrates the local features of CNNs and the capability of capturing long-range dependencies of transformers to model both local and global dependencies for ship recognition in an efficient way. The cosine-margin loss is also applied to constraint strictly the distribution of the features to further improve the performance. Experimental results on the benchmark FUSAR-Ship dataset demonstrate the effectiveness of the proposed method for ship ATR in HR SAR images.

The Microwave Photonic (MWP) radar, designed for ultrawideband signal emission in high-precision imaging, encounters challenges in ultra-high-resolution images where strong scattering points obscure weak edge scattering areas of the target. Consequently, the resultant images exhibit isolated strong points instead of continuous edges on the physical structure, diminishing the interpretative capacity of highresolution radar images. This paper proposes a Microwave Photonic Inverse Synthetic Aperture Radar (MWP-ISAR) edge recovery method based on morphological theory to address this issue. The method employs image preprocessing, including dilation and edge detection, followed by the hough transform (HT) for accurate edge localization. Subsequently, An adaptive neighborhood enhancement operator is used to restore the target’s edge features. Notably, the method adeptly tackles the computational cost problem arising from numerous parameter estimates in the image reconstruction process based on parametric models while also automatically extracting edges for restoration. Finally, the proposed method undergoes validation and quantitative evaluation using authentic aircraft data, substantiating its effectiveness in enhancing radar image interpretation.

Synthetic Aperture Radar (SAR), with its all-weather and all-day operation, is an effective tool for earth observation. However, with the continuous intensification of global changes, current earth observation methods face challenges in meeting the demands for global coverage and timeliness. Moon-based SAR (MBSAR) has the advantage of long observation time and wide coverage. However, the imaging capability of MBSAR is limited by the orbit characteristics of the moon. Using the moon as the illumination source and the high-earth orbit (HEO) satellite as the receiving station (MH-BISAR) not only allows for a wide imaging area and flexible viewing angle, but also can effectively reduce the signal transmission power. This paper first establishes the motion model of MH-BISAR in a unified coordinate system, then calculates the basic conditions such as the required transmit power for MH-BISAR and compares it with MBSAR. Next, the imaging capabilities of MH-BISAR and imaging time of global areas within one month are analyzed. Finally, it is concluded that MH-BISAR has the advantages of high resolution, long observable time, large imaging coverage, and low system requirements for earth observation. It can serve as a powerful means for earth observation.

Electromagnetic jamming can seriously affect the quality of synthetic aperture radar (SAR) images and pose significant obstacles to image interpretation. The additional degrees of freedom brought by multistatic SAR considerably contribute to the accurate extraction of jamming information. In this paper, a jamming detection and jammer localization method for the interrupted sampling repeater jamming (ISRJ) is proposed. First, the jamming components in multistatic SAR images are detected by utilizing the time delay characteristics of ISRJ. Then, based on the Doppler frequency invariance property of jamming, a system of equations for multiple receiving configurations is solved for jammer localization. The effectiveness of the proposed method is demonstrated through simulation.

In complex synthetic aperture radar (SAR) imaging configurations, such as bistatic SAR, time-domain algorithms are less constrained and more accurate than frequency-domain algorithms. But they have not be applied to real-time imaging well because of large computation. The back-projection algorithm based on wavenumber-domain spectral splicing (WFBP) that emerged recently can be used to resolve this contradiction. In this paper, an efficient implementation architecture of this algorithm is designed based on FPGA. Parallel structures are used for sub-aperture BP imaging and images fusion. The imaging results and speed of the system is verified by simulations and experiments. It runs WFBP over a image of size 1024×512 in 0.131s, significantly accelerating imaging while maintaining accuracy of time-domain algorithms.

In high sea states, ship targets often exhibit intense and complex rotational movements due to sea waves, leading to defocused and time-varying imaging results for bistatic synthetic aperture radar (SAR). This paper introduces a novel strategy for selecting optimal imaging times of ship targets by analyzing image projection plane (IPP). Utilizing short-time Fourier transform (STFT) on target echoes, rotation parameters of the ship are estimated via differential evolution (DE) method, to determine IPP throughout the entire observation period, so that the optimal imaging moments can be selected. By comparison of image contrasts and entropies, the optimal imaging time lengths are also refined. Overall, this paper offers an optimal time section strategy ensuring clear views of ship targets, and simulation results verify its effectiveness.

The separated platforms of a bistatic synthetic aperture radar (SAR) introduce synchronization error in the echo, resulting in offset and defocused target position and other characteristics. To deal with the problem, this paper firstly analyzes the synchronization error characteristics, then proposes a synchronization error separated method based on variational mode decomposition techniques, and ultimately uses the echo after the error separation to get the high precision SAR imaging results. Simulation experiment results verify the effectiveness of the proposed method.

With the increasing widespread use of synthetic aperture radar(SAR) systems in various environments, jamming have become a serious problem that they face. In many situations, these jamming affect SAR systems’ ability to gather information. Many anti-jamming methods are based on the classification of jamming types. In order to provide information about jamming types, it is necessary to design a classification method which can classify multiple types of jamming. Considering the complexity of jamming features, in this paper, multi-domain jamming feature analysis and a residual network with convolutional block attention module (CBAM) are proposed to classify SAR jamming. To train this jamming classification network and validate its effectiveness, a database containing different jamming simulations is generated. The simulation results show that this method has reliable classification performance for various types of jamming.

Airborne multistatic synthetic aperture radar (SAR) shows various advantages such as high flexibility and target observation from different perspectives regarding localization. However, due to the separation of transmitters and receivers, the time-frequency synchronization error is induced and the traditional Range-Doppler (R-D) localization method is unreliable because of challenges in Doppler centroid estimation. Furthermore, there is a position error in the position of the unmanned aerial vehicle (UAV) as a high-maneuvering platform. This paper proposes a novel localization method for airborne multistatic SAR based on time difference of arrival (TDOA). This method only uses range information extracted from received signals without Doppler centroid estimation for TDOA localization equation construction. Then the localization equation is solved by the Taylor algorithm for the target location, which is of good robustness to the time-frequency synchronization error and the UAV position error. Numerical simulations validate the effectiveness of this method.

Bistatic synthetic aperture radar (BiSAR) can provide high-resolution images and effectively observe from various visual angles, enabling automatic target recognition. Estimation of target scattering parameters is one of the important key techniques in target recognition and super-resolution imaging. The compressed sensing(CS)-based methods have been widely used in SAR imaging. However, the hyperparameters of these methods are often difficult to be set to optimal, which leads to inaccurate estimation results. To solve this problem, this paper proposes a BiSAR target scattering parameter estimation method. First, the echo model is established based on the point scattering model and three typical scattering primitives to characterize the different scattering characteristics of different targets. Second the scattering parameter estimation problem is transformed into the optimization problem, and the alternating direction method of multipliers(ADMM) is introduced to estimate target parameters. There are four regularization parameters in the optimization problem, which are automatically set to the optimum by combining with the differential evolution(DE) algorithm. Then the estimated result is obtained by optimization with the optimal regularization parameters. Numerical simulation experiments demonstrate the effectiveness of the proposed method.

Multipass synthetic aperture radar (SAR) tomography is a promising method for 3-D imaging. However, atmospheric turbulence-induced motion errors lead to severe 2-D defocusing and hinder 3-D imaging. The spatial-variant characteristic of motion errors further complicates motion compensation. To address this issue, an improved autofocus method is proposed, which achieves 2-D focusing by reconstructing trajectory. Firstly, a spatial-variant phase error compensation model is established to elucidate the correlation between motion errors and 2-D image quality. Subsequently, to maximize image sharpness, motion errors estimation is performed using a combination of the block coordinate descent (BCD) algorithm and the gradient descent (GD) algorithm. Finally, image registration is applied to correct 2-D image offset induced by an image-quality-based optimization strategy. Simulation results confirm the effectiveness and superiority of the proposed method.

Video synthetic aperture radar (SAR) has attracted increasing attention in recent years due to its ability to provide continuous images for the scenes of interest. However, practical applications of video SAR are limited by the large amount of data and computational costs involved in imaging. In this paper, we propose a deep unfolding network for reconstructing SAR videos from undersampled echo data. Firstly, we introduce a low-rank representation operator to perform low-rank representation on the video SAR tensor. Then the problem of reconstructing video SAR is modeled as a regularization problem based on low-rank representation and solved by the alternating direction method of multipliers (ADMM) algorithm iteratively. Finally, we unfold the iterative solution into a deep neural network to learn the network parameters and low-rank representation operator from the data. Simulation experiments validate the effectiveness of the proposed method.

This letter presents a novel method to resolve the ambiguity in forward-looking radar systems. Traditional methods for ambiguity suppression face challenges with antenna deviations and the ill-conditioning of measurement matrices. To address these limitations, we introduce a deviation-adaptive unfolding network (DAUNet), which integrates perturbed compressive sensing (PCS) and advanced deep learning techniques. The DAUNet efficiently handles the space variance of the measurement matrix by utilizing matrix direct summation and the Kronecker product and modeling the ambiguity suppression as a unified PCS problem. This method incorporates iterative learning processes and a novel neural network architecture to reconstruct ambiguity-free images from multichannel forward-looking radars. Simulation results demonstrate that our approach significantly outperforms the existing methods in handling both point targets and distributed scenarios.

Synthetic aperture radar (SAR) is capable of obtaining the high-resolution 2-D image of the interested target scene, which enables advanced remote sensing and military applications, such as missile terminal guidance. In this article, the terminal trajectory planning for SAR imaging guidance is first investigated. It is found that the guidance performance of an attack platform is determined by the adopted terminal trajectory. Therefore, the aim of the terminal trajectory planning is to generate a set of feasible flight paths to guide the attack platform toward the target and meanwhile obtain the optimized SAR imaging performance for enhanced guidance precision. The trajectory planning is then modeled as a constrained multiobjective optimization problem given a high-dimensional search space, where the trajectory control and SAR imaging performance are comprehensively considered. By utilizing the temporal-order-dependent property of the trajectory planning problem, a chronological iterative search framework (CISF) is proposed. The problem is decomposed into a series of subproblems, where the search space, objective functions, and constraints are reformulated in chronological order. The difficulty of solving the trajectory planning problem is thus significantly alleviated. Then, the search strategy of CISF is devised to solve the subproblems successively. The optimization results of the preceding subproblem can be utilized as the initial input of the subsequent subproblems to enhance the convergence and search performance. Finally, a trajectory planning method is put forward based on CISF. Experimental studies demonstrate the effectiveness and superiority of the proposed CISF compared with the state-of-the-art multiobjective evolutionary methods. The proposed trajectory planning method can generate a set of feasible terminal trajectories with optimized mission performance.

With the echo difference between different channels, the left/right ambiguity in single-channel forward-looking synthetic aperture radar (SAR) can be resolved using dual-channel radar. However, due to the restricted channel resources, existing methods have limited performance in resolving left/right ambiguity, especially in the area along the flight path. In this article, a novel left/right ambiguity resolving scheme is proposed to improve the imaging performance of dual-channel forward-looking SAR. In the scheme, the steering matrix considering the time-frequency–space information of echo was designed first, and the linear mapping equation between the echo and the original scene was established. Then, the regularized iterative adaptive approach (RIAA) is introduced to solve the equation, thereby achieving dual-channel forward-looking SAR left/right ambiguity resolving and superresolution imaging simultaneously and directly in the echo domain. At last, the simulated and experimental results were illustrated to demonstrate the effectiveness of the proposed scheme.

Bistatic synthetic aperture radar (BiSAR) has high geometric diversity and can obtain the target information from different observation angles. With the ability of azimuth beam steering for both platforms, different bistatic imaging modes can be implemented to achieve better cooperation of beam footprints for enhanced imaging performance. For the multimode general bistatic SAR with complex trajectory (MGCT-BiSAR), due to the different beam steering methods and the translational-variant geometry, the spatial variance of the Doppler centroid becomes complicated, which leads to a severe azimuth spectrum aliasing problem. Moreover, the complex trajectory of MGCT-BiSAR may introduce high-order range cell migration (RCM) and Doppler parameters, which leads to the 2-D coupling and the different Doppler characteristics for different beam steering modes. The existing imaging algorithm cannot uniformly process the multimode SAR data. This article proposes an improved polar formatting algorithm based on minimum azimuth spectrum width (minASW-PFA) to uniformly process the bistatic SAR data. The proposed method first calculates the unified deramping factors and proposes a minASW bulk deramping method to minimize the azimuth spectrum width of the different imaging modes. However, the process leads to a severe range-Doppler coupling, which cannot be fully eliminated by the traditional polar formatting and wavefront curvature compensation methods. Therefore, an improved polar format mapping and wavefront curvature compensation method are also proposed to solve the coupling problem and achieve high-order spatial variance compensation. Finally, numerical simulations verify the proposed algorithm to achieve unified imaging for multimode general bistatic SAR with complex trajectories.

High-resolution synthetic aperture radar (HR-SAR) is extensively used in ground remote sensing applications, including disaster monitoring and resource crop assessment. This is attributed to its exceptional high-resolution imaging capabilities. Microwave photonics technology plays a crucial role in enhancing the performance of SAR systems. It enables the direct emission of ultra-wideband signals across multiple frequency bands. However, this advancement also makes microwave photonic synthetic aperture radar (MWP-SAR) susceptible to complex and diverse electromagnetic interference. Particularly, it is vulnerable to radio frequency interference (RFI), and this interference seriously affects the high-resolution imaging results. In order to solve these problems, an MWP-SAR imaging algorithm based on depth spectral sensing and spectral reconstruction that is suitable for complex electromagnetic environments is proposed. Simultaneously, a sensing-removing-recovering (SRR) anti-interference imaging framework is established. To address the high-precision detection of various unknown interferences in MWP-SAR echoes, a deep learning network is constructed. This network is based on spectrum sensing theory and achieves an interference detection probability greater than 98% in various interference scenarios. Subsequently, targeting the continuous spectrum loss in the MWP-SAR signal after interference removal, a signal reconstruction algorithm is proposed. This algorithm employs Toeplitz transformation for signal loss scenarios, facilitating the recovery of signals with extensive spectrum loss post-interference removal. The factor group sparse regularization (FGSR) algorithm is used to quickly solve the signal recovery problem. Through simulation and measured data processing, the superiority of the proposed algorithm over existing interference suppression imaging algorithms is demonstrated.

Due to its all-day and all-weather capability, synthetic aperture radar (SAR) plays an important role in many remote sensing and monitoring applications. However, conventional SAR image reconstruction methods generally perform undifferentiated imaging, complicating target detection. To address this challenge, we propose a SAR image reconstruction method based on self-attention deep prior learning for differentiated image reconstruction and target detection. The proposed method can separate the target from the clutter using their feature priors during image reconstruction, thus helping improve target detection performance. Specifically, a deep prior learning operation based on a self-attention convolutional neural network (SACNN) is proposed. SACNN can help enhance target and suppress clutter by learning both the local and global features. Finally, the proposed method is implemented through an unrolled deep network with a loss function designed to make the reconstructed image beneficial for target detection. Simulation experiments have been conducted to verify the efficacy of the proposed method.

Sea clutter suppression plays a crucial role in maritime moving target indication. However, in the bistatic SAR (BiSAR) system, traditional space-time adaptive processing (STAP) method can’t satisfy the expected performance due to severe range cell migration (RCM), Doppler frequency migration (DFM), nonstationary clutter, and spatio-temporal spectrum expansion caused by the internal motion of sea clutter. STAP based on sparse recovery (SR-STAP) is an effective method for clutter suppression, but two major problems still remain. (1) The multiple samples for solution need to satisfy the same spatio-temporal distribution characteristics. Nevertheless, such consistency is not applicable when considering violent internal motion of sea clutter. (2) The computational complexity is exceedingly high. To issue these problems, an efficient matrix sparse recovery STAP (MSR-STAP) method based on Kronecker transform is proposed. The proposed method mainly consists of three steps: (1) Generalized Keystone transform in preprocessing stage is used for RCM correction and DFM compensation. (2) Multiple spatio-temporal samples acquisition strategy for CUT is designed, to enhance the solution robustness. (3) An efficient MSR-STAP model is established and solved. Subsequently, the space-time filter is designed without clutter covariance matrix estimation, to facilitate effective sea clutter suppression. Compared with existing SR-STAP methods, computational complexity of the proposed method decreases by orders of magnitude, and the spatio-temporal spectrum expansion effect is greatly reduced. The sea clutter suppression performance is verified with numerical simulations.

Localization and tracking are important components for ground-moving target indication (GMTI). The traditional range-Doppler (RD) localization model is always applied to locate stationary targets in bistatic synthetic aperture radar (BiSAR). However, for moving targets, this localization model is no longer applicable due to the strong coupling of position and velocity with Doppler frequency. To address the severe challenge, this article proposes a joint moving target localization and tracking method for BiSAR-GMTI via transmitter–receiver trajectories extraction and inversion. The key innovation is the derivation of closed-form localization results for moving targets, which is beneficial for the quantitative analysis of localization error. The proposed method is structured into three main parts. First, to accurately capture the range history trajectory information, a segmented fitting extraction and inversion framework is designed. Second, to estimate the moving target’s initial state and demonstrate localization observability, joint range localization equations are established where the closed-form localization result of the moving target is derived. Finally, by defining the appropriate state transition equation and observation equation, and incorporating particle filtering (PF), the position measurement errors of bistatic platforms are weakened, and accurate tracking of moving targets is realized. The proposed method only utilizes range information, mitigating localization errors caused by Doppler estimation inaccuracies effectively. Simulation experiments and real data both demonstrate the localization and tracking accuracy of moving targets.

Radar forward-looking imaging has important applications in autonomous landing, autonomous navigation, reconnaissance guidance and other fields. However, conventional single channel synthetic aperture radar (SAR) or Doppler beam sharpening (DBS) technology has a blind area for forward-looking imaging due to left/right ambiguity and small angle variation. Multichannel radar can utilize the differences of echoes from multiple channels in azimuth to resolve left/right ambiguity, and has the potential for forward-looking imaging. However, there is still a problem of low azimuth resolution due to the restriction of array size. In this article, a deep learning based multichannel radar forward-looking super-resolution imaging framework is proposed. In this framework, synthetic aperture processing is conducted on the echo data of each channel to obtain the image with left/right ambiguity, and the preliminary forward-looking imaging is achieved first by resolving left/right ambiguity with multichannel data. Then, the generative adversarial network (GAN)-like network with mixed attention mechanism is designed to learn the mapping relationship between the original scene and the preliminary imaging result. At last, based on the learned mapping relationship, the echo data of multichannel radar is processed with the proposed framework to achieve forward-looking superresolution imaging. Experimental results were provided to verify the effectiveness of this imaging framework.

This article presents a robust and efficient method for recovering missing data in sub-Nyquist synthetic aperture radar (SAR) using matrix completion (MC) techniques. Unlike previous MC-based methods that have limitations on the required data properties, such as broadside-looking mode, sparse target scenarios, distributed missing, or low missing data ratios (MDRs), our method is capable of handling consecutively clustered missing data and high MDR, even in the presence of weakened spectrum sparsity caused by squint-looking mode or dense target scenarios. Our approach involves arranging the incomplete SAR echo data from a single range-time sampling instant into a Hankel structured matrix and recovering it using nuclear-norm-based convex relaxation and an augmented Lagrangian multiplier (ALM) method. To address computational complexity, we propose segmenting the SAR echo data into smaller slices and providing guidelines for selecting an appropriate slice size. We demonstrate the effectiveness and accuracy of our method through extensive experiments using simulated data and realistic Radarsat-1 data. The experiments cover various challenging conditions, including squint-looking mode, dense target scenarios, wide-swath coverage, high MDR, and clustered missing. The results highlight the capabilities of our method in handling sub-Nyquist SAR data recovery, even in challenging scenarios. Our proposed method offers a robust and efficient solution for recovering missing SAR data.

Swarm unmanned aerial vehicle synthetic aperture radar (UAV SAR) system leverages multiple UAVs to form a formation, overcoming the limitations of a single platform and enabling the execution of advanced SAR missions. By forming a uniform linear array formation, the swarm UAV SAR system is able to coherently enhance resolution in one direction. Extend to 2-D cases, a uniform planar array needs to be formed for resolution enhancement. However, the requirement for a large number of UAVs to form the planar array can lead to significant costs. In addition, the performance of resolution enhancement is intricately tied to the chosen system formation. Therefore, there is a pressing need to conduct research on methods to obtain the optimal formation. In this article, a hybrid resolution enhancement (HRE) framework has been proposed for the swarm UAV SAR system to optimize resolution enhancement performance while mitigating costs. The proposed framework is mainly divided into two stages: cost-effective formation strategy and optimal HRE. The cost-effective formation strategy, which lays down the foundation for resolution enhancement, is comprised of three steps. First, to achieve HRE with a reduced number of UAVs, a cross-shape formation structure is established. Second, to effectively optimize the position and velocity of the central node of the UAV swarm for optimal resolution performance, a constrained differential evolution (DE)-nondominated sorting (CDE-NS) algorithm is proposed. Third, baseline design is conducted to determine the attached nodes’ positions for optimal coherent resolution enhancement (CRE). After the ideal formation is obtained, optimal HRE can be accomplished. Specifically, the principle of CRE is explained. The inspiration, motivation, and novelty of the proposed noncoherent resolution enhancement method named minimum combination (MC) are elucidated. Simulation results have demonstrated the validation of the proposed framework.

Bistatic synthetic aperture radar (BiSAR) parametric imaging can reconstruct the structural information of the target, which is one of the research hotspot for BiSAR imaging. However, compared with monostatic SAR, there are several challenging problems to be faced in terms of BiSAR parametric imaging, such as complex echo models, compute and storage burden, azimuth-dependent phase (ADP). To this end, we first propose the BiSAR parametric echo model, followed by analysis of the characteristics of BiSAR parametric imaging. Based on the abovementioned anaysis, we propose a deep adaptive BiSAR parametric imaging network (DAPI-net), which consists of three parts, namely adaptive dimensionality reduction module, parameter estimation module and image reconstruction module. The main idea of DAPI-net is to employ coarse imaging to determine the approximate range of target parameters and construct a local observation matrix to reduce computational complexity. On this basis, a variant observation matrix learned solver and ADP compensation unit are used to achieve high-precision BiSAR target parameter estimation. Among them, the ADP compensation unit mainly mitigates echo model mismatch caused by ADP and improves the parameter estimation performance. Finally, BiSAR parametric image output is achieved through image dilation. The efficacy of DAPI-net is validated through simulation experiments and microwave anechoic chamber data. The proposed algorithm not only addresses the identified challenges but also advances the state-of-the-art in BiSAR parametric imaging.

Geosynchronous synthetic aperture radar (GEO-SAR) can provide long-duration and wide beam coverage over the interested target scene, which is an ideal illuminator for bistatic SAR acquisitions. As a particular system configuration, the GEO bistatic SAR with maneuvering platform as the receiver (GEO-MP-BiSAR) can achieve continuous observation of the interested target during the flight. The target recognition and tracking information can be generated from the updating images for enhanced guidance performance. However, the bistatic SAR imaging performance is dependent on the observation geometry, which in turn is determined by the trajectory of the receiver. Therefore, in this paper, the trajectory optimization for GEO-MP-BiSAR is firstly investigated. The goal of the method is to generate a set of feasible trajectories to guide the maneuvering platform towards the target, and meanwhile obtaining the optimized imaging performance during the whole flight. The trajectory optimization is then modeled as a multi-objective optimization problem with multiple constraints, where the trajectory control and SAR imaging performance are comprehensively considered. Then, a knee-guided multiobjective evolutionary algorithm is put forward to effectively solve the problem, where the knee solutions are utilized to guide the search process and improves convergence and diversity of the method. The proposed algorithm can generate the prescribed number of solutions with significant trade-offs between the performance metrics. The mission designer can then choose a solution from only a few optimized candidates for implementation, which greatly improves the efficiency of decision making. Experimental studies demonstrate the effectiveness of the proposed method.

A conventional synthetic aperture radar (SAR) transmits the linear frequency modulation (LFM) signal for imaging and processes the target and clutter without distinction, making target enhancement difficult to realize. Waveform design is considered a common approach to improve the signal-to-clutter ratio (SCR). However, due to the undulating frequency response of radar clutter, the waveform designed for SCR improvement tends to have a high sidelobe autocorrelation function (ACF), which can lead to the degradation of SAR imaging performance. To solve this problem, this article proposes a complementary SAR waveform set design method for the improvement of the SCR while suppressing the sidelobe. Different from the traditional single waveform design method, in this article, the transmitted waveform set is jointly optimized. The designed waveform with a complementary sidelobe varies between pulses. Utilizing the multipulse azimuth compression of SAR imaging, the aforementioned high sidelobe can be eliminated after azimuth focusing. To this end, we use the inexact alternating direction penalty method (IADPM) and develop the spectrally constrained complementary sequences (SCCSs) algorithm to solve the resulting nonconvex optimization problem. Simulation and experimental data verification highlight the effectiveness of the proposed design for SCR improvement while maintaining the SAR imaging performances.

Synthetic Aperture Radar (SAR) is widely used in various fields due to its all-weather and all-day working characteristics. With the increasing use of SAR on small platforms, SAR is facing a series of problems due to the large volume of echo data. Imaging methods based on compressed sensing (CS) use the sparsity prior of the scene to reconstruct images from undersampled echoes. However, the CS-based method requires the imaging scene or its transformation domain to be sparse, which is not the case for most practical applications. This paper proposes a deep unrolling network named NSR-NET, which is based on SAR image representation learning and is applicable for undersampled imaging in non-sparse scenes. In modeling, the learned image representation is adopted as the regularization term. Then, the proximal gradient descent (PGD) algorithm was used to derive the iterative solution of the model. In network design, the iterative process is unrolled into a deep neural network with learnable parameters. Specifically, image representation is obtained through 2D convolutional layers in the network, and a learnable piecewise linear layer is used to fit the regularization function, which ultimately achieves the mapping from undersampled echoes to SAR images. Comparative experiment using different imaging methods shows that the imaging performance of the proposed network exceeds that of the state-of-the-art methods in non-sparse scenes. Moreover, we also designed transferability validation experiments with different radar parameters and imaging scenes, whose experimental results suggest that the proposed network has good generalization ability.

Sidelobe suppression is an important step in SAR imaging, especially for forward looking areas with poor azimuth resolution. In this paper, the signal model of multichannel forward-looking SAR imaging is realized with BP algorithm firstly, and then spatial smoothing coherence factor(SSCF) is introduced to suppress sidelobes of the imaging results. At last, simulation results are given to illustrate the effectiveness of the proposed scheme.

Different sea states make maritime ship targets move three-dimensionally, causing imaging results severely defocused. Therefore, selection of proper imaging time has attracted global attention in the field of ISAR imaging. This paper proposes an imaging time and plane selection algorithm based on short-time fractional Fourier transform (STFrFT). By STFrFT, time-frequency curves of the target echo for different fractional orders are obtained, and probability density functions of frequency distributions are offered to estimate when Doppler frequency of the target changes most stably. Then, different imaging planes of different STFrFT orders are analyzed to select the optimal imaging time moment. Finally, by comparing image contrasts of various imaging lengths, the optimal imaging time is selected. In general, this method can select the optimal imaging moment, imaging length and imaging plane, and simulation verifies the effectiveness of this imaging method.

The actual detection scenes faced by bistatic synthetic aperture radar (SAR) often have elevation information, which will cause shadow and overlay effects in imaging results. In view of the above problems, this paper proposes an echo generation method of bistatic SAR based on hidden point removal(HPR) operator. In this paper, according to the bistatic configuration, the shadow area is deduced first by using blanking algorithm(HPR operator). On this basis, the visibility of the target in the scene can be determined to obtain the echo. Finally, a simulation using a digital elevation model is conducted to verify the accuracy of the proposed method.

In UWB (ultra-wideband) radar imaging, there are differences in the signal scattering characteristics of targets for different frequencies. The estimation of target scattering characteristics is an important research field in target recognition and structured imaging. To solve this problem, this paper combines the target scattering characteristic estimation and super-resolution imaging into a low-rank and sparse signal reconstruction problem. It proposes an ADMM (the alternating direction algorithm of multiplier) scattering characteristic estimation algorithm based on low-rank and sparse signal. First, the echo model of UWB signal is established according to GTD model. Then build the corresponding complete dictionary. Finally, the ADMM-LS algorithm is used to solve the multi-objective joint optimization problem. Numerical simulation experiments verify the algorithm to prove the effectiveness of the proposed method.

In the application of vehicular MIMO (Multiple Input Multiple Output) radar, it is particularly important to measure and associate the position and velocity of targets. However, traditional methods like 3D-FFT have low angular resolution, and velocity ambiguity resolution is required. Combined with Super-resolution DOA (Direction Of Arrival) algorithm, we propose a multi-dimensional information association method based on target tracking, which uses the labeled GM-PHD (Gaussian Mixture Probability Hypothesis Density) algorithm. This method simultaneously completes the association task about position and velocity and the tracking task, and does not require an additional step on velocity ambiguity resolution. Finally, we use experimental data to verify the effectiveness of the algorithm.

Conventional monostatic SAR or DBS technology cannot realize forward-looking imaging due to Doppler symmetry ambiguity. With multiple channels in azimuth, multichannel radar can resolve the left/right ambiguity, and have the potential for forward looking imaging. However, multichannel radar have flexible channel configuration, and there are different processing schemes, which may have different imaging performance and challenges. In this papar, based on the signal model of SIMO radar, different processing schemes for multichannel radar forward-looking imaging are illustrated and simulated, as well as the corresponding potential and challenges.

In the past, the parameters of SAR system were relatively fixed, which lead to the poor flexibility in the use of imaging resources. In this paper, the concept of cognitive radar is introduced into SAR, which can make the system adaptively optimize the resources required. We design a three-steps process for large-scale imaging, which consists of two reconnaissance process and one cognitive processing link. Finally, we design the parameters and resources of two reconnaissance process manually or by using genetic algorithm. Experimental results show that the parameters designed meet the demand of large-scale search and the genetic algorithm used can significantly reduce the resource cost in further reconnaissance and improve the flexibility of system.

Moving target indication (MTI) plays an important role in both military and civilian applications. However, the performance of MTI has deteriorated dramatically due to clutter interference. In bistatic synthetic aperture radar (BiSAR), MTI mainly faces three challenges: First, BiSAR has large range cell migration (RCM); Second, the Doppler spectrum is severely extended; Third, the clutter presents nonhomogeneity and nonstationarity. In order to solve these problems, a novel MTI method for single channel BiSAR is proposed. At first, the first-order keystone transform (KT) is performed to remove linear range walk (RWK) regardless of unknown motion parameters. Then, the high-order RCMC is realized by reference compensation function. It is extremely difficult to achieve MTI because moving target is submerged in clutter whether in azimuth time domain or Doppler domain. So, to achieve separation of moving target from stationary clutter effectively, a novel procedure named increased dimension rotation (IDR) processing is introduced. Thereafter, the distribution characteristic of moving target and stationary clutter after rotation processing is analyzed exhaustively. Naturally, the optimal filter is constructed for clutter suppression in the light of the distribution characteristic. Finally, the energy of moving target can be accumulated after inverse increased dimension rotation and Fractional Fourier Transform (FrFT). And the MTI in BiSAR can be easily achieved.

The information obtained by single sensor is limited, so the fusion of multi-source images becomes the research focus. Monostatic SAR and bistatic SAR can obtain complementary scattering information from the same target, but the SAR image lacks color information resulting in limited visual effects. The optical image contains color information but it is difficult to achieve high resolution at the decimeter level due to the limitations of present optical cameras. Therefore, this paper proposes an algorithm to fuse monostatic and bistatic SAR image with optical image. First, the three images are registered, then the monostatic and bistatic images are stitched into a dual-channel image. Finally, the dual-channel SAR image is fused with optical image using a blind model-based fusion method. The fusion result combines the color information of optical image with the high-resolution and texture information of SAR image, which enhances the visual effect and readability, providing great convenience for subsequent applications. The fusion results of the experimental data show that the algorithm can effectively make the image information more comprehensive and accurate.

Multi-core digital signal processor (DSP) is widely used in SAR real-time imaging system for its high-speed operation capacity and parallel working ability. The matching of the algorithm and the parallel architecture has great impact on imaging speed of SAR processing. This paper proposed an efficient imaging architecture based on multi-core DSP TMS320C6678. The system implements bistatic polar format algorithm (PFA), using two-dimensional Chirp-Z Transform and one-dimensional interpolation to realize two-dimensional resampling. The experimental results show that the proposed architecture can complete 1024×1024 points echo processing and output a 1024×1024 pixels image within 1.7 seconds.

The joint FPGA and multi-DSP imaging architecture has found widespread use in airborne SAR and satellite-based SAR. However, traditional back-projection (BP) algorithms are not efficient enough for real-time imaging. This paper proposes a back-projection algorithm based on wavenumber-domain spectral splicing (WFBP) for designing an efficient SAR imaging system. A unified polar coordinate system is employed in this work to project the sub-aperture imaging results, which eliminates the need for a large amount of interpolation and multiple projection transformations. Furthermore, spectral shifting is applied to eliminate the effects caused by wavenumber-domain spectral overlap. Experimental results demonstrate that the WFBP algorithm reduces the imaging time by 64% compared to the traditional BP algorithm.

Multistatic SAR based on UAV swarm can reduce synthetic aperture time and increase the imaging frame rate while maintaining high image resolusion, thus it is of great promising prospect. Due to the lack of efficient and high precision imaging methods, the performance of UAV swarm SAR is not fully developed. Therefore the imaging method suitable for UAV swarm SAR is studied in this paper. Compared with frequency domain imaging techniques, time domain imaging method is more appropriate for UAV swarm SAR with complex trajectories. But the heavy computational burden is a problem. Thus this paper proposes a fast imaging method based on spectrum fusion for high frame rate UAV swarm SAR. In the method, each bistatic pair in UAV swarm SAR is split into several sub-pairs to generate coarse angular resolution images and the fusion of images is realized through spectrum fusion, which obtains the imaging result of UAV swarm SAR. Experiment is carried out to verify the effectiveness of the proposed method.

Multistatic synthetic aperture radar (SAR) can obtain abundant information from different angles for terrain classification and tomography. However, multistatic SAR systems, particularly the multistatic global navigation satellite systems (GNSS) face the problem of insufficient resolution. To address this issue, a novel noncoherent combination method termed as minimum combination (MC) is proposed. Inspired by dual apodization, MC is specifically designed to improve the resolution of the multistatic SAR system. Relative to the conventional noncoherent addition (NA) method, MC yields an image with higher resolution and reduced sidelobe level. Simulation results are presented to illustrate the effectiveness and superiority of the proposed approach.

Multichannel SAR can solve the problem of left/right ambiguity and has the potential of high-resolution forward-looking imaging. However, its azimuth superresolution in the adjacent area of flight path is still poor due to the small angle variation. In this paper, we propose a multichannel forward-looking SAR azimuth superresolution method based on CycleGAN. First, the multichannel forward-looking SAR imaging results are obtained, and then the mapping between forward-looking imaging results and the ground truth is learned with CycleGAN, to achieve azimuth superresolution. At last, simulation results are given to illustrate the effectiveness of the proposed method.

Multichannel radar has the potential of forward-looking imaging with multiple channels receiving echoes on a single platform, but its azimuth resolution is usually poor. Superresolution algorithms have been developed to solve the problem, however, most of the methods have problems of difficulty in parameter adjustment and large amount of computation. In this paper, driven by the powerful learning ability of deep networks, the conventional ISTA reconstruction process is mapped into a deep network, and then the deep unfolding ISTA-Net is formed and used to realize multichannel radar forward-looking superresolution imaging. The simulation reults verify that the proposed method can provide high-quality reconstruction results while substantially reducing the imaging time.

Conventional monostatic SAR or DBS technology cannot realize forward-looking imaging due to Doppler symmetry ambiguity. Although the ambiguity can be resolved by using multiple channels in azimuth, its azimuth resolution in the vicinity of flight path is usually poor due to the small angle variation. To solve the above problems, a hybrid real/synthetic aperture scheme for multichannel radar forward-looking imaging is proposed in this paper. In the scheme, the synthetic aperture imaging result with left/right ambiguity is obtained firstly using the information of platform motion. Then the real aperture superresolution imaging with regularized iterative adaptive approach(RIAA) is achieved using the instantaneous data of multiple channels. At last, the two imaging results are fused to obtain the forward-looking superresolution image without left/right ambiguity. Simulation results are given to illustrate the effectiveness of the proposed scheme.

Shadow simulation is a key part of SAR raw data and image simulation for undulated terrain, which will directly determine the accuracy of SAR simulation and will also affect the accuracy of subsequent shadow-based SAR image processing applications. In this paper, a shadow simulation scheme for undulated terrain SAR images based on facet cell fitting and elevation angle comparison is proposed. In the scheme, the height information of each grid point on the same line of sight within the beam irradiation range is obtained firstly with the facet cell fitting, and the elevation angle of each grid point can be computed and compared to determine whether the occlusion happens. Then, all grid points in the illumination range are traversed to obtain the shadow judgment result at a certain azimuth moment, and the SAR shadows are obtained by superimposing the judgment results at all azimuth moments. At last, simulated and measured data experiments are given to verify the effectiveness of the proposed scheme, and the results show that this scheme can accurately simulate the SAR shadow of undulating terrain.

Retrieval of building height from synthetic aperture radar (SAR) image is of great significance for damage assessment after natural disasters, urban development and planning, and dynamic time series monitoring in urban areas. Based on the prior information of the building and the radar platform, the building height can be estimated by iteratively simulating the SAR image and matching it with the measured SAR image. However, the estimation accuracy would be affected inevitably when there is error of prior information introduced by instable platform or inaccuracy of measuring instruments. In this letter, the beam incident angle and building height are both used as search variables for iterative simulation and matching (ISM), and normalized mutual information (NMI) is used to measure the similarity between the simulated image and the measured image to achieve an accurate estimation of building height. At last, measured data experiments are provided to verify the effectiveness of the proposed scheme.

Highly constrained multiobjective optimization problems (HCMOPs) refer to constrained multiobjective optimization problems (CMOPs) with complex constraints and small feasible regions, which are commonly encountered in many real-world applications. Current constraint-handling techniques will face two difficulties when dealing with HCMOPs: 1) feasible solution is hard to be found and too much search effort is spent in locating the feasible region and 2) since the total feasible region of an HCMOP can consist of several disconnected subregions, the search process might be stuck in the comparatively larger feasible subregion, which does not contain the whole Pareto front (PF). To address these two issues, an evolutionary algorithm with constraint relaxation strategy based on differential evolution algorithm, that is, CRS-DE, is proposed in this article. In each generation, the CRS-DE relaxes the constraints by dividing the infeasible solutions into two subpopulations based on total constraint violation, that is, the "semifeasible" subpopulation (SF) and "infeasible" subpopulation (IF), respectively. The SF provides information on the promising regions of finding the feasible solution and is the driving force for convergence toward the PF, while the IF focuses on global exploration for new promising regions. Corresponding reproduction and selection strategies are devised for the SF, IF, and feasible subpopulations, which create a clear division of labor with cooperation to facilitate the search for feasible solutions. To leverage the influence of CRS and prevent the population from premature convergence, a mobility restriction mechanism is developed to restrict the individuals in the SF and IF from entering the feasible subpopulation and enhance the diversity of the whole population. Comprehensive experiments on a series of benchmark test problems and a real-world CMOP demonstrate the competitiveness of our method compared with other representative algorithms in terms of effectiveness and reliability in finding a set of well-distributed optimal solutions for HCMOPs.

In our earlier study, an energy-efficient passive UAV radar imaging system was formulated, which comprehensively analyzed the system performance. In this article, based on the evaluator set, a mission planning framework for the underlying energy-efficient passive UAV radar imaging system is proposed to achieve optimized mission performance for a given remote sensing task. First, the mission planning problem is defined in the context of the proposed synthetic aperture radar (SAR) system and a general framework is outlined, including mission specification, illuminator selection, and path planning. It is found that the performance of the system is highly dependent upon the flight path adopted by the UAV platform in a 3-D terrain environment, which offers the potential of optimizing the mission performance by adjusting the UAV path. Then, the path planning problem is modeled as a single-objective optimization problem with multiple constraints. Path planning can be divided into two substages based on different mission orientations and low mutual correlation. Based on this property, a path planning method, called substage division collaborative search (Sub-DiCoS), is proposed. The problem is divided into two subproblems with the corresponding decision space and subpopulation, which significantly relax the constraints for each subproblem and facilitates the search for feasible solutions. Then, differential evolution and the whole-stage best guidance technique are devised to cooperatively lead the subpopulations to search for the best solution. Finally, simulations are presented to demonstrate the effectiveness of the proposed Sub-DiCoS method. The result of the mission planning method can be used to guide the UAV platform to safely travel through a 3-D rough terrain in an energy-efficient manner and achieve optimized SAR imaging and communication performance during the flight.

As high-frame-rate synthetic aperture radar (SAR) has the ability to form continuous SAR images and dynamically monitor the ground areas of interest, it has attracted more and more attention nowadays. In practical applications, the enormous data in high-frame-rate SAR system to obtain the multiframe images brings big challenges to its transmission, storage, and processing. In order to solve this problem, there are many recent papers on formulating the high-frame-rate SAR imaging problem into a low-rank tensor recovery problem, and correspondingly, the sampling amount of the high-frame-rate SAR data can be largely reduced. However, existing algorithms to solve the low-rank tensor recovery problem in high-frame-rate SAR application still suffer from large computational cost. Under the above inspiration, this article proposes a deep neural network architecture for high-frame-rate SAR imaging, i.e., tensor alternating direction method of multiplier network (TADMM-Net), which is more computationally efficient in the imaging procedure. Specifically, we formulate the high-frame-rate SAR imaging processing into a low-tubal-rank tensor recovery problem. We solve the low-tubal-rank tensor recovery problem using a tensor alternating direction method of multiplier (ADMM) algorithm and then design a new deep neural network architecture by applying algorithm unfolding techniques to the underlying low-rank tensor recovery problem. The proposed TADMM-Net approach shifts the computational burden from the testing phase to the training phase, and the practical processing time can be extremely decreased compared with the existing algorithms for the low-rank tensor recovery problem in high-frame-rate SAR imaging applications. It also offers various advantages over the existing high-frame-rate SAR imaging algorithms, including higher performance in the case with low sampling amount, lower storage complexity, and no requirement for handcraft hyperparameters adjustment. The methodology was tested on high-frame-rate SAR data. These tests show that the proposed architecture outperforms other state-of-the-art methods in high-frame-rate SAR imaging applications.

In the beam steering bistatic synthetic aperture radar (BS-BiSAR) system, both the transmitter and receiver beams can be steered in the azimuth direction, offering increased flexibility in mission planning to meet specific imaging requirements. Compared with the current SAR system, the system resources of BS-BiSAR have a high degree of freedom (DOF), including bistatic configuration, beam steering modes, and pulse repetition frequency (PRF), which are closely related to imaging performance. With appropriate beam steering strategy and observation geometry, better cooperation of beam footprints of the platforms can be achieved for enhanced imaging performance. The aim of this paper is to explore the resource management problem (RMP) for BS-BiSAR and optimize the comprehensive system performance, including imaging area, spatial resolution, signal ambiguity and radiometric performance. The RMP is then formulated as a single-objective problem subject to multiple constraints. An improved particle swarm optimization method combined with a sentry learning strategy is proposed to solve the optimization problem under strict constraints and high dimensional solution space. Simulation experiments are conducted to validate the effectiveness of the proposed method for managing system resources and optimizing performance. The result of the resource management method can be applied to guide the system design and achieve the comprehensive performance optimization of BS-BiSAR.

Compared to conventional microwave regime radars, microwave photonic (MWP) radar is capable of transmitting extremely large bandwidth signals, wherein the frequencies of such signals distribute across multiple bands. In practical applications, the large bandwidth of MWP radar may be split into multiple discrete sub-bands due to various considerations such as anti-jamming, resource-saving, communication band avoidance, etc. Nonetheless, it leads to the fact that MWP radar suffers from the challenging problems of side-lobes elevation and main-lobes broadening. These problems will affect the image quality seriously. In order to address this issue, a spectrum recovery algorithm based on an improved Truncated Schatten-pNorm and Sparse Regularizer-Alternating Direction Method of Multipliers (TSPN-ADMM) network is proposed in this paper. This algorithm can efficiently recover the lost spectrum in MWP radar applications and further improve the imaging quality of the MWP radar. In the lost spectrum recovery problem, the parameters of the recovery algorithm directly determine the recovery performance. The different forms of lost spectrum possessed by MWP radar make the selection of parameters for the spectrum recovery algorithm extremely difficult. As a consequence, in this paper, the spectrum recovery problem for MWP radar can be reformulated into a matrix completion problem by exploiting its joint sparsity and low-rankness. Based on the traditional TSPN-ADMM algorithm, an improved TSPN-ADMM-Net approach is proposed by utilizing the algorithm unrolling technique, wherein the hyperparameters in TSPN-ADMM algorithm are optimized in an end-to-end training manner. Consequently, the algorithm proposed in this paper can achieve excellent recovery results when dealing with the multiple spectrum missing situations existing in MWP radar. The effectiveness of the algorithm is verified by a combination of numerical simulations and actual MWP radar data.

Synthetic aperture radar (SAR) plays an important role in remote sensing by providing electromagnetic images of the observation scene. The prior knowledge-based SAR image reconstruction method can reduce the requirement of data sampling ratio and improve image quality. The existing prior knowledge-based method usually uses the magnitude information of SAR images while ignoring the phase information. However, since the echo and backscattering coefficients are complex values, the phase information of SAR images will help improve the reconstruction accuracy in the image reconstruction process. To improve the reconstruction performance, this paper proposes a SAR image reconstruction and autofocus method using complex-valued feature prior. In the proposed method, the complex-valued feature prior is learned from data by a complex-valued feature projection operator (CFPO), which can characterize and extract scene features in Range-Doppler domain and 2D frequency domain. The proposed CFPO enables more efficient use of echo data and helps to improve image reconstruction and autofocus performance. In addition, the proposed method is implemented by an unfolded deep network, which enables data-driven feature learning and efficient computation. The proposed method is verified by simulated and measured data.

Despite its widespread use in Earth remote sensing, synthetic aperture radar (SAR) image reconstruction remains challenging. The difficulties mainly lie in the handling of diverse scenes and motion errors with sparsely sampled data. Existing matched filtering (MF)-based methods cannot handle sparsely sampled data, while regularization-based methods lack adaptability to scene diversity. Although deep learning-based SAR methods can deal with these two issues, their performance will be degraded by motion errors. To address this, we propose a Transformer-based SAR image reconstruction method called RATIR-Net. The proposed method can obtain SAR images of various scenes under sparse sampling and motion errors by learning the correlations between echo data. In RATIR-Net, CNN-based encoding and decoding blocks are constructed to implement azimuth processes of range profiles (RP) in the range-Doppler domain according to the MF-based method. Meanwhile, a Residual Attention Transformer (RAT) block is designed to extract correlations between RPs, compensating for information loss caused by sparse sampling and suppressing non-correlated perturbations caused by motion errors. The CNN-based encoding and decoding blocks help reduce computing costs, and the RAT block mitigates the dependence on scene features and the influence of motion errors. These make RATIR-Net efficient and effective. Simulation experiments have been conducted to verify the proposed method.

Moving target indication (MTI) in bistatic synthetic aperture radar (BiSAR) is a promising task in both civilian and military fields. However, it suffers severe challenges under the influence of clutter. MTI in BiSAR mainly faces three challenges: 1) the range cell migration (RCM) of BiSAR is larger than that of monostatic synthetic aperture radar; 2) the clutter range-Doppler spectrum is severely extended; and 3) the clutter characteristic is closely related to geometrical configuration, with nonhomogeneity and nonstationarity. To solve these problems, based on single-channel BiSAR, a joint clutter suppression and MTI method is proposed. First, to ensure that the energy of an arbitrary target is concentrated in one range cell, RCM correction (RCMC) is completed by the first-order keystone transform (KT) and high-order RCMC. Then, an important step named increased dimension rotation (IDR) is applied, which mainly consists of two stages. One is increased dimension processing, which introduces a 2-D azimuth rotated time domain (ARTD). The other is rotation processing, which makes signals in one range cell rotated to 2-D ARTD. After that, the distribution characteristic between the moving target and stationary clutter in 2-D ARTD is analyzed. Next, an optimal filter for clutter suppression is designed according to the characteristics of signal distribution in 2-D ARTD. Furthermore, the corresponding inverse IDR processing and fractional Fourier transform (FrFT) are performed, and finally, MTI can be realized accurately. Generally, the proposed method has good robustness and low system complexity, and its effectiveness is proven by numerical simulations.

Despite the success of compressive sensing (CS) algorithms in sub-Nyquist SAR imaging, they lack a generalized ambiguity-depression capability and suffer from high computational complexity. To address these weaknesses, we propose a novel imaging approach that simultaneously suppresses ambiguity introduced by nonuniform sampling and non-ideal azimuth antenna patterns, while enhancing resolution performance for wide-swath sub-Nyquist SAR imaging. To improve efficiency, we employ the randomized block coordinate descent based on the accelerated fast iterative shrinkage threshold algorithm (FISTA) to obtain the desired ambiguity-free image. In the accelerated FISTA, we incorporate two measures to reduce computational complexity. Firstly, we analytically formulate the update stepsize using the singular-value perturbation theory, Kronecker product technique, and orthogonality of the uniformly discrete Fourier matrix. This enables efficient computation of the Lipschitz constant. Secondly, we expedite the computation of the gradient matrix through efficient frequency domain techniques. Numerical results using both simulated data and realistic SAR echo validate the effectiveness of our proposed method and demonstrate its advantages over existing algorithms.

Ship recognition in synthetic aperture radar (SAR) images is essential for many applications in maritime surveillance tasks. Recently, convolutional neural network (CNN)-based methods tend to be the mainstream in SAR recognition. Though considerable developments have been achieved, there are still several challenging issues toward superior ship recognition performance: 1) Ships have a large variance in size, making it difficult to recognize ships by using a single scale features of CNN. 2) The SAR ship’s large aspect ratio presents an obvious geometric characteristic. However, standard convolution is limited by the fixed convolution kernel, which is less effective in processing elongated SAR ships. 3) Existing CNN classifiers with softmax loss are less powerful to deal with intraclass diversity and interclass similarity in SAR ships. In this paper, we propose a task-specific hierarchically designed network with a spherical space classifier (HDSS-Net) to alleviate the above issues. Firstly, to realize SAR ship recognition with large size variation, a feature aggregation module (FAM) is designed for obtaining a feature pyramid that has strong representational power at all scales. Secondly, a FeatureBoost module (FBM) is devised to provide rectangular receptive fields to refine the features generated by FAM. Finally, a novel spherical space classifier (SSC) is proposed to expand the interclass margin and compress the intraclass feature distribution by fully taking advantage of the property of spherical space. The experimental results on two benchmark datasets (OpenSARShip and FUSAR-Ship) jointly show that the proposed HDSS-Net performs better than classic CNN methods and novel SAR ship recognition CNN methods.

Bistatic synthetic aperture radar (BiSAR) has been attracting worldwide attention because of its forward-looking imaging and high anti-interference. In harsh environment, it is vital for BiSAR to conduct extensive surveillance, imaging, and recognition of maritime ship targets. However, under the disturbance of sea waves, the ship target has an unknown and massive three-dimensional (3-D) rotation, so that its imaging projection plane (IPP) is also undetermined. Thus, high-dimensional random distortion appears in imaging results, making it difficult to recognize the target through two-dimensional distorted images effectively. To solve these problems, bistatic SAR maritime ship target 3-D image reconstruction method without distortion in local Cartesian coordinate (LCC) is proposed. In this paper, according to positions of scatterers and rotation parameters, significant differences of different scatterers of maritime ship targets have been found in bistatic range, Doppler centroid (DC), and Doppler frequency rate (DFR), which lays a solid foundation for the scatterer separation of ship targets. On this basis, a 3-D R-DC-DFR domain is constructed, and 2-D echoes of the maritime ship target are projected into R-DC-DFR domain to separate scatterers. Then, by remapping data of transmitter and receiver in R-DC-DFR domain to LCC, as well as evaluating their similarity metric, the optimal rotation parameters of the ship target can be obtained via the maximal similarity. Therefore, the image distortion caused by the unknown IPP has been removed, and 3-D image reconstruction of ship targets can be realized without distortion in the LCC. Furthermore, to evaluate performances of 3-D image reconstruction for different rotation parameters and bistatic configurations, 3-D reconstruction index is proposed and analyzed. Both point-targets and maritime ship targets are simulated to emphasize the effectiveness of the proposed method.

GEO spaceborne-airborne bistatic SAR (GEO SA-BiSAR) has flexible configuration and the ability of multi-looking imaging, so it has a good application prospect. The transmitting propagation delay is about 0.1s due to the 36500Km high altitude transmitter, so the motion of ground target and receiver in the propagation delay can't be ignored. This paper presents a range model under the “non-stop-and-go” assumption, which analyzes the transmitting and receiving process respectively, and we consider the movement of the target and receiving platform under long transmitting delay, and obtains a succinct and accurate range model. For the delay of each process, we establish the accurate numerical solution, the approximate solution and expanded of the time delay under the “non-stop-and-go” assumption separately. Simulation results show that our method can estimate the real propagation delay accurately for both stationary and moving targets.

Multistatic synthetic aperture radar (SAR) can realize 3D imaging of observation scenes by single navigation using distributed aperture. Meanwhile, due to the flexible baseline configuration of unmanned aerial vehicle (UAV), and has broad application prospects in remote sensing, surveying and mapping fields. However, the 3D reconstruction performance of multistatic SAR is closely related to its multistatic baseline. In this paper, a multistatic baseline design method is proposed to achieve optimal 3D reconstruction performance. Firstly, the quantitative relationship model between multistatic baseline and 3D imaging measurement matrix is established, and the cross-correlation value of measurement matrix is introduced as the evaluation index of reconstruction performance. Then, the multistatic baseline design problem is modeled as an optimization problem with an optimal crossrelation number. Finally, the differential evolution algorithm is used to obtain the multistatic baseline of the optimal design. Simulation results show that compared with the unoptimized multistatic baseline, the optimized multistatic baseline can obtain better reconstruction performance when the typical sparse recovery method is used for 3D imaging.

Beam steering is widely applied in the airborne and space-borne SAR system to achieve a balance between large imaging area and high azimuth resolution. However, it leads to a linear variation of Doppler centroid in monostatic SAR, which is more complicated in bistatic SAR due to the contribution built by the transmitter and the receiver and results in Doppler spectrum aliasing. The spatial variance of the Doppler centroid of the beam steering bistatic SAR is analysed. And a modified preprocessing method for azimuth-variant bistatic SAR with beam steering mode is proposed to remove the Doppler spectrum aliasing by effectively increasing the sampling rate of azimuth time. The movement of the platforms is considered with cured trajectory.

Multistatic SAR based on unmanned aerial vehicles (UAV) swarm can simultaneously obtain the echo of the target from different perspectives. Thus it can synthesize a large aperture in a short time, realizing high resolution and high frame rate imaging of interested areas. But there is few imaging method for high frame rate UAV swarm SAR and the problem of over-lapping apertures affecting imaging quality remains to be re-solved. Therefore, this paper proposes a time-domain imaging method to solve the problems. Firstly, echo model of UAV swarm SAR is established and multistatic equivalent aperture distribution is analyzed. Then the echo is equalized to im-prove image quality. Next, coarse angular resolution images of each bistatic SAR pair in UAV swarm SAR is accurately formed. Finally, all bistatic images are recursively fused to get the final result. Simulation results verify the effectiveness and accuracy of the proposed method.

Airborne ultra-wideband synthetic aperture radar (UWB-SAR) is easily affected by motion error, leading to a decline in image quality. In this paper, a motion error compensation method based on coherent correlation function (CCF) is proposed, which can estimate the high-order components of motion error and reconstruct platform trajectory. Taking the peak sidelobe ratio (PSLR) of CCF as the evaluation function, the particle swarm optimization (PSO) algorithm is used to estimate the high-order components of the trajectory error. Then the trajectory is modified to compensate for the image blur caused by motion error. The effectiveness of the method is verified by numerical simulation.

Synthetic Aperture Radar (SAR) can perform all-time and all-weather observations and is wildly used in earth remote sensing. The sparsity-driven SAR imaging methods can reconstruct sparse scenes under down-sampling conditions, but they are unsuitable for non-sparse scenes. To reconstruct non-sparse scenes from under-sampled data and further improve the utilization efficiency of sampled data, this paper proposes a feature learning and SAR imaging method and implements it through a deep network. The imaging model is constructed firstly, where a feature-based sparsity regularization term is incorporated. Then, by unfolding the iterative solution derived via the Alternating Direction Multiplier Method (ADMM) algorithm, a CNN-based deep network is proposed to solve this imaging model. In the proposed network, convolution layers are used to represent and learn the scene feature prior knowledge. Simulation experiments verify the effectiveness of the proposed method.

Various imaging methods based on compressed sensing (CS) of synthetic aperture radar (SAR) have been proposed to reduce the sample size of echoes required for the imaging process. The unrolling technique further solves the inefficiency of conventional CS-based methods by mapping them into deep neural networks. However, most of these methods are based on sparsity prior of the scene or its transformation domain, which could be invalid for non-sparse scenes. To address this, we proposed a network utilizing the feature priors of the images instead of sparsity for non-sparse scene reconstruction of SAR, namely NSR-Net. We adopt learnable regularization terms in the CS model. Then the iterative solving process of the model is derived and unrolled into the proposed deep neural network to learn the best regularization terms from data. Simulation experiments verified the effectiveness of NSR-Net in the reconstruction of non-sparse scenes with down-sampled SAR echoes.

Synthetic aperture radar (SAR) can obtain two-dimensional images of the illuminated area, which is an important means for earth remote sensing and monitoring. However, due to the loss of azimuth data and system errors during the processing of data sampling, it is necessary to study the method for high-quality SAR image reconstruction from down-sampled data in the condition of measurement inaccuracy. Considering these factors, this paper proposes a sparsity-driven SAR imaging method based on general regularization and the sparse total least-squares (S-TLS) model and implements the method by an unfolded deep network. In the proposed method, general regularization can solve the problem of sparse sampling, and the S-TLS model is adopted to deal with measurement inaccuracy. Moreover, through the deep network implementation, the proposed is more time-efficient and can exploit more effective scene prior knowledge, making the proposed method suitable in practical applications. Experiments verify the effectiveness of the proposed method.

Synthetic Aperture Radar(SAR) image registration technology is widely used, but some problems are exhibited in the available technologies, such as feature extraction, loss of phase information, low computational complexity, and so on. To cope with these problems, in this paper, a method regards target Attributed Scatter Center (ASC) as the feature is proposed. ASC model highlights electromagnetic characteristics, which can better represent SAR images than optical characteristics. First, the parameter set of target ASC model is estimated by Iterative Shrinkage Thresholding Algorithm (ISTA), as a stable point feature registration descriptor of the image. Then, Nearest Neighbor Distance Ratio (NNDR) method is used to find the feature matching points of the reference image and the image to be registered. The matching accuracy is improved by the Random Sample Consensus (RANSAC) method. Finally, by transformation model and image offset, image registration and phase retention are realized. The simulation results demonstrate that the proposed method enables a better registration performance since the scattering characteristics are effectually utilized.

Localization plays an important part in the application field of bistatic SAR (BiSAR). The accuracy of traditional localization method for BiSAR depends on the measurement precision of platform. The lower measurement precision, the higher localization error of targets. However, due to the size, cost, and technical limitations of existing attitude measurement devices, it is difficult to satisfy the requirement of high-precision target positioning in BiSAR. This paper proposes a blind locali-zation method based on monostatic equivalent model for BiSAR, which has high tolerance and low sensitivity for measurement error. First, the BiSAR is equivalent to monostatic SAR system based on the principle of Equivalent Phase Center (EPC). Next, the Range-Doppler localization model is applied to fix position for EPC and the relative position of multi-targets are obtained subsequently. At last, establishing localization equation with bistatic range of multi-targets, and the Newton iteration algorithm is devoted to solving localization model constructed above. The effectiveness of the localization method proposed in this paper has been demonstrated and illustrated by numerical simulations.

In recent years, many imaging algorithms for highly squint synthetic aperture radar (SAR) have been proposed. An algorithm based on keystone transform (KT) and azimuth Extended Nonlinear Chirp Scaling (ENLCS) is widely used in highly squint SAR imaging processing. It's effective in solving spatial-variant linear range cell migration (LRCM) and azimuth-variant Doppler parameters. However, due to the highly squint configuration, the beam center crossing time of many illuminated targets are not included in the track. We need extend the azimuth data length to ensure the targets a corresponding position in the data, which leads to an increase in computational complexity. And the imaging result also has geometric distortion. This paper proposes an improved ENLCS method with lower complexity combined with fast KT. First, we use the low complexity KT without interpo-lation in the RCM correction (RCMC) process. Then, we find a solution to reduce the extended data length by adding a time shift factor in the ENLCS process, saving data storage space and operation cost. Finally, geometric correction is performed by the grid mapping. The effectiveness of the proposed method is verified by numerical simulation and real data processing.

Distributed MIMO Array Radars normally utilize sparse arrays with large equivalent array element spacing, which cannot avoid the generation of grating lobes that cause interference to the received signal. The grating lobes can be suppressed by Phase Coherence Factor (PCF) weighting factor. Regretfully, PCF has problems such as the possibility of suppressing the main lobe as well and the unclear phase resolution of the grating lobes. In this paper, we propose a new weighting factor, namely Embedded Classification Coherence Factor (ECCF), by improving the PCF weighting factor, and propose an embedded superposition method to filter and superimpose the phases of all channels in the BP imaging process. ECCF shows a lower peak side lobe ratio than PCF, and enjoys a superior effect on grating lobe suppression. Simulations are given to verify the performance.

Sparse linear array synthetic aperture radar (SAR) is widely used in near-field 3-D imaging, which can reduce the volume of data and the cost of acquisition. However, the sparsity of array elements means the missing rows or columns of data, leading to the high-level sidelobes and the aliasing of targets. Therefore, in this paper, we develop a near-field 3-D imaging method for non-uniform sparse linear array SAR based on matrix completion (MC), where a rotated expansion algorithm is proposed to make the non-uniform sparse data af-ter range compression satisfies the requirements of the MC. The MC technique is applied to the signal slice of each range cell to reconstruct the echo signal, and the back-projection algorithm (BP) is used to achieve 3-D high-resolution imaging of targets. Moreover, simulations and near-field experi-ments validate the feasibility and superiority of the proposed method.

Clutter suppression is the premise of moving target detection and imaging. We propose a cancellation method for GEO Spaceborne–Airborne Bistatic SAR (GEO SA-BiSAR). Firstly, according to the range history under the “non-stop-and-go” assumption, we establish a multi-channel echo signal model. Then, according to the range from the high orbit transmitting station to the target, we compensate the phase of the echo signal. Next, by analyzing the multi-channel phase relationship, we propose an improved Displaced Phase Center Antenna (DPCA) method suitable for GEO SA-BiSAR, and then we analyze the result and performance of DPCA. Finally, the effectiveness of this clutter suppression method is verified by numerical simulation.

High sidelobe of strong scattering points usually submerges weak targets nearby and affects the quality of SAR image. Therefore, SAR image usually requires sidelobe control. Common window functions have limited improvement on PSLR performance when the image resolution is required to be guaranteed. Combining Min-Max weighted ISL technique, this paper proposes an azimuth low sidelobe window function design method for SAR. Simulation results show that PSLR of the designed window is nearly −10dB lower than hanning window with a −45dB ISL level, and main lobe width is almost equal to hanning window.

With the continuous development of imaging radar technology, the bandwidth of transmission signals continues to increase. The geometrical theory diffraction (GTD) model can describe target scattering characteristics under wideband electromagnetic wave irradiation, which is significant for target detection and recognition. Existing target scattering center parameter estimation methods based on compressed sensing (CS) theory require sparse imaging scenes, which is difficult to meet in actual radar data. To solve this problem, this paper proposes a method for estimating high-value scattering centers parameters based on energy trajectory extraction. The estimation result of this method is accurate with fewer operations than CS-based method, and this method reduces the requirement for target sparsity. And use numerical simulation to prove the effectiveness of the method.

Synthetic Aperture Radar (SAR), an all-weather microwave remote sensing technology, is widely used in environmental monitoring, earth resource surveys and other fields, especially in the military field to achieve monitoring purposes. In the monitoring process, it is crucial to achieve real-time imaging of the target scene. Therefore, in this paper, an efficient SAR real-time imaging system is designed to achieve timely parallel processing of the scene when the radar platform scans the target scene, so as to reduce the consecutive frame cycles of real-time imaging. In which, the signal processing board structure consists of two FPGAs and six DSPs is used to quickly implement high precision SAR real-time imaging for airborne radar, so that the radar system can display the target image in time with the platform moving and check whether the current echo is valid. In addition, the architecture proposed in this paper can be extended in case the continuous frame period for real-time imaging is short.

Benefiting from the capability of forward-looking imaging, ability of receiver radio silence and resistance of jamming, bistatic SAR has extensive potential applications. However, due to its independent dual platform movement, motion error of bistatic SAR is usually more complicated than monostatic SAR and could easily exceed range resolution cell, which will result in both azimuth and range defocusing if not properly compensated. In this paper, an autofocus scheme for bistatic SAR considering cross-cell residual range migration is proposed. In this scheme, cross-cell residual range migration is firstly compensated by estimating bistatic range error through time-frequency analysis. Meanwhile, azimuth phase error is coarsely compensated by using bistatic range error estimation result. Secondly, maximum image sharpness autofocus method is cascaded to further compensate for azimuth residual phase error and enhance the quality of bistatic SAR image. The effectiveness of proposed scheme is verified by simulation and experiment results.

Synthetic aperture radar (SAR) imaging with sub-Nyquist sampled echo is a challenging task. Compressed sensing (CS) has been widely applied in this case to reconstruct the unambiguous image. The CS-based methods need to set the iterative parameters manually, but the appropriate parameters are usually difficult to obtain. Besides, such methods require a large number of iterations to obtain satisfactory results, which seriously restricts their practical applications. Moreover, the observation scene of SAR is not sparse in some cases. In this paper, we aim at proposing an efficient and effective imaging method for non-sparse observation scenes with reduced data. Firstly, considering the characteristics of non-sparse observation scenes in SAR imaging, we model the SAR imaging problem as a joint low-rank and sparse matrices recovery problem. After that, the iterative alternating direction method of multipliers (ADMM) to solve the above problem is unrolled into a layer-fixed deep neural network with trainable parameters, in which the learnable parameters are layer-varied. The threshold parameters, as well as the weight parameter between the sparse part and low-rank part of each layer, are learned adaptively instead of manually tuned. Experiments prove that the proposed LRSR-ADMM-Net is capable of reconstructing the non-sparse observed scene with high efficiency and precision. Particularly, the proposed LRSR-ADMM-Net yields better reconstruction performance while maintaining high computational efficiency compared with the state-of-the-art iterative recovery methods and the trainable sparse-based network methods.

Video synthetic aperture radar (SAR) receives more and more attention in recent years because it can provide continuous images of the observed scene. However, the enormous data of video SAR to obtain the multiframe images bring big challenges to its transmission, storage, and processing, especially for small unmanned aerial vehicle (UAV) platform. In this article, we aim at proposing an efficient video formation method for video SAR systems with reduced data. First, the characteristics of video SAR observed scene are analyzed. It is found that the observed scene with multiple frames can be modeled as the sum of a low-rank tensor and a sparse tensor efficiently. After that, the video formation problem for video SAR is modeled as a joint low-rank and sparse tensors recovery problem. Finally, an efficient tensor alternating direction method of multiplier is proposed to obtain the final SAR video. Compared with the traditional frequency- or time-domain imaging methods, the amount of data samples can be greatly reduced. On the other hand, the proposed method outperforms the state-of-the-art SAR imaging methods with reduced samples, including the joint low-rank and sparse matrices recovery method and the low-rank tensor recovery method. Numerical simulations validate the effectiveness of the proposed method.

Geosynchronous spaceborne–airborne bistatic synthetic aperture radar (GEO-SA-BiSAR) consists of a GEO transmitter and airborne receiver, which has extensive application prospects in both civilian and military fields for its ability to generate high-resolution images of the ground target with frequent coverage and abundant scattering information. However, the Doppler bandwidth in this configuration exceeds the transmitted pulse repetition frequency (PRF), which leads to sub-Nyquist sampling. To solve this problem, a multireceiving technique has been applied to the receiver to increase the equivalent sampling rate and reconstruct an unambiguous image. In this article, we take a different approach to recover the unambiguous image for GEO-SA-BiSAR with fewer receiving channels. First, the accurate echo model is established based on the “non-stop-and-go” propagation delay model to lay the foundation of accurate imaging. After that, the GEO-SA-BiSAR imaging problem is modeled as a problem of joint sparse and low-rank matrices’ recovery. To reduce the computing time of the traditional recovery method, a modified alternating direction method of multipliers (M-ADMM) is proposed, where the computation and storage of the computational expensive observation matrix are avoided. Furthermore, an M-ADMM method with multiple receiving channels, which combines the recovery theory and multireceiving information, is also proposed to handle the severe sub-Nyquist sampling echo of GEO-SA-BiSAR. Simulation results reveal that the proposed method can recover the original image scene with high computational efficiency. Meanwhile, the number of receiving channels can be reduced compared with the multireceiving technique.

Microwave photonic synthetic aperture radar (MWP-SAR) offers a larger signal bandwidth than conventional SAR, and its theoretical resolution can be improved to centimeter level. To achieve same order of magnitude resolution in azimuth direction, long synthetic aperture is always required, which results in extremely high requirements for the accuracy of imaging procedure. Compared with conventional SAR imaging algorithms, two major problems should be considered: 1) The scattering characteristics of target might vary from the frequency of signal and the angle of incidence, which seriously affects the coherence of received echo. 2) The imaging of MWP-SAR system is more sensitive to motion errors and therefore requires higher accuracy of motion compensation processing. To solve the above issues, a high-precision imaging method for MWP-SAR is proposed. First, based on the attribute scattering center(ASC) model, this paper analyzes the target-scattering characteristics with different frequencies and incident angles. Then, according to the influence of scattering phase on MWP-SAR imaging, an optimal sub-aperture division algorithm is proposed to guarantee the coherence of each sub-aperture data and better imaging results. Furthermore, to compensate for the effects of high-order motion errors, this paper proposes a motion error estimation algorithm based on sub-image registration, where the full aperture high-order motion errors can be divided into multiple linear components, and the flight trajectory of platform can be accurately reconstructed. Finally, a full-aperture time-domain high-precision imaging method is presented, and the effectiveness of the proposed formation is verified by both simulation and actual airborne MWP-SAR data processing.

The global navigation satellite system (GNSS)-based passive radar shows potential in permanent maritime surveillance. In this paper, the GNSS signals are exploited for vessel target imaging. From the obtained radar image, meaningful information about the vessel, such as its shape, position, length, and orientation can be extracted. In addition, the vessel is observed from different angles by spatially diverse GNSS satellites, and the multistatic geometry enables to enhance the imagery quality. The main drawback of GNSS-based passive radar stays in its limited power budget. And the inaccessible motion makes the noncooperative vessel smeared using conventional radar imaging methods. To address the problems, at first, each bistatic echo over a long observation time is integrated in range and Doppler (RD) domain after removing the two-dimensional migrations. The signal-to-noise ratio can be increased after the step. Then, with respect to a particular target velocity, the local Cartesian plane is constructed, and the multiple RD maps are projected and combined in the plane to obtain the multistatic image. In view of the inaccessibility of target kinematic parameters, such imaging processing is modeled as an optimization problem, where vessel’s velocity is set as decision variable and the aim is to minimize the image entropy. Finally, particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which a well-focused vessel image can be obtained. In May 2021, we have successfully carried out the world’s first BeiDou-based passive radar maritime experiment, and effectiveness of the proposed method is verified against the experimental data.

This article puts forward a BeiDou-based passive multistatic radar (PMR) maritime moving target (MMT) detection technique via space–time hybrid integration (STHI) processing. Compared with passive bistatic radar (PBR), the utilization of multiple satellites provides an improvement in MMT detection performance, together with the capabilities of localization and velocity estimation. However, the multiple satellite transmitters cause the differences principally in bistatic range and Doppler centroid (DC) of the MMT. To integrate the PMR echoes, the biggest challenge is the two differences that need to be handled. In the proposed technique, first, the centroid-compensated keystone transform (CCKT) is proposed and applied to each PBR echo. It not only corrects range cell migration (RCM) but also equalizes the DC to the same. Then, the long-time integration is performed on each PBR echo, after which it is integrated into the range-Doppler frequency rate (DFR) domain. Finally, in order to settle the difference in bistatic range, an MMT position and velocity domain (i.e., the$X$–$Y$–$V$domain) is constituted. The obtained multiple range-DFR maps are projected to the$X$–$Y$–$V$domain, and then, the effective integration of multistatic echoes can be implemented. The final STHI result allows detecting the MMT reliably. Meanwhile, according to the 3-D position where the MMT is located in the$X$–$Y$–$V$domain, the MMT can be localized, and its velocity can be estimated simultaneously. In May 2021, we have successfully carried out the world’s first BeiDou-based PMR MMT detection experiment, and the experimental results are given to prove the effectiveness of this technique.

Synthetic aperture radar (SAR) is an important means for target surveillance through reconstructing the microwave image of the observation area. However, under the condition of low signal-to-clutter ratio (SCR), such as a strong sea clutter situation, it is difficult to surveil targets from SAR images acquired by the traditional matched filter-based imaging methods. To improve the target surveillance performance of SAR, this article proposes a target-oriented SAR imaging method, which can enhance the desired target and improve the SCR in the reconstructed SAR images. By separating the target area from the clutter area, we first establish a target-oriented SAR imaging model, where the generalized regularization is used to characterize the features of the target, contributing to the improvement of SCR in the reconstructed image. Then, the imaging model is solved through a deep network, MF-ADMM-Net, which is obtained by unfolding an alternating direction method of multipliers (ADMM)-based iterative solution. In addition, the training strategy is formulated with the consideration of complex values. Experiments are conducted to verify the performance of image reconstruction and SCR improvement of the proposed method, and comparisons show the superiority of MF-ADMM-Net in effect and efficiency.

Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering images of the illuminated area, playing a significant role in various applications. However, achieving focused SAR images is challenging under sparse sampling and phase error conditions. By exploiting the sparsity or compressibility priors, the state-of-the-art sparsity-driven SAR imaging methods can reconstruct images under the condition of sparse sampling. However, the handcrafted priors used in these methods limit the imaging performance, and the iterative solution schemes reduce the computational efficiency. Besides, the measurement inaccuracy introduced by the phase error also degrades the reconstruction performance of the sparsity-driven imaging methods. To address these issues, a deep network for SAR autofocus imaging is proposed, which alternately performs image reconstruction and phase error estimation. When performing image reconstruction, the sparsity-cognizant total least-square (S-TLS) model is introduced to handle the problem of measurement inaccuracy, contributing to robust reconstruction performance under the condition of phase error. During the implementation of the deep network, a feature transform operator is used to realize data-driven prior knowledge learning and overcome the limitations of handcrafted priors. Moreover, the deep network approach can significantly improve computational efficiency. Experiments on simulated and real data verify the effectiveness and efficiency of the proposed method.

Clutter suppression is a challenging task in synthetic aperture radar-ground moving target indication (SAR-GMTI). In general, sufficient secondary samples are not easily acquired due to the nonstationary and nonhomogeneous characteristics of bistatic SAR (BiSAR) clutter, resulting in worse clutter suppression results. Recently, space–time adaptive processing based on sparse recovery (SR-STAP) has been developed since its better clutter suppression performance with less samples. However, since the off-grid problem in space–time domain caused by BiSAR’s separate configuration, existing SR-STAP would suffer from severe performance degradation. To address this problem, a clutter-ridge matched STAP (CRM-STAP) method for BiSAR nonstationary clutter suppression is proposed. First, clutter distribution modeling with arbitrary BiSAR configuration is applied to accurately obtain the clutter ridge in space–time domain. Then, keystone transform and time-division processing are applied to correct range cell migration and eliminate Doppler frequency migration, respectively. Next, to solve the off-grid problem, the CRM dictionary is reconstructed via adaptive gradient method, which is established along the direction of clutter ridge and its orthogonal direction. Then, with the constructed CRM dictionary, the clutter covariance matrix (CCM) estimation process is transformed to a multimeasured vector optimization problem, and it can be directly solved by the sparse Bayesian learning algorithm. Finally, based on the estimated CCM, the CRM-STAP filter is built to suppress the nonstationary clutter effectively. Compared with the existing STAP and SR-STAP methods, this method can avoid the performance degradation in clutter suppression caused by the off-grid problem and overcomes the strong nonstationary problem of BiSAR clutter in heterogeneous environments. In October 2020, we have successfully carried out the world’s first airborne BiSAR-GMTI experiment, and the experimental results are given to verify the effectiveness of this method.

Bistatic forward-looking synthetic aperture radar (BFSAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of the receiver. Current literature and reports about BFSAR mainly concentrate on the stationary scene and ground-moving target imaging. Unlike stationary and ground-moving targets, the translational and rotational movements of ship targets usually lead to complicated range cell migration (RCM) and Doppler frequency migration (DFM). Moreover, the characteristics of RCM and DFM for different scattering points of the ship target are significantly different, i.e., the characteristics of the RCM and DFM are 2-D spatial variation, ultimately leading to severe defocusing of ship target in the SAR image. To solve these problems, a kind of hybrid SAR-ISAR imaging formation is proposed for BFSAR ship target imaging. First, to solve the problem of the Doppler ambiguity caused by the forward-looking mode of the receiver, an efficient ambiguity estimation method based on the minimum entropy criterion is presented. Then, keystone transform and range alignment processing can be applied to correct the spatial variant range walk and higher order RCM, respectively. Moreover, in order to obtain a high-resolution and well-focused image after translational compensation, a new method based on the fractional Fourier transform (FrFT) and the Wigner–Ville distribution (WVD) is proposed, where FrFT is applied to separate the multiple main scattering points in each range cell, and WVD is applied to obtain the high-resolution time–frequency distribution of each scattering point. Compared with the conventional ISAR range-Doppler (RD) algorithm and time–frequency estimation-based imaging methods, this method not only has no cross terms but also has high processing accuracy and better antinoise performance.

Due to the unequispaced pulse repetition interval (PRI), the low-oversampling ratio and the range-variant blockage, the echo of the low-oversampled staggered SAR (LS-SAR) is nonuniformly sampled with sub-Nyquist and range-variant rate. However, the existing LS-SAR processing methods lack robustness with regards to the scenario type and the PRI variation mode. In this article, a compressive-sensing-based image reconstruction method for the LS-SAR is proposed. First, a hybrid-domain model (HDM) of the LS-SAR echo is presented. In the HDM, the coupled range cell migration (RCM), the unequispaced PRI, and the conflict blockage are formulated as the matrix multiplications with a 3-D tensor, a 2-D matrix, and a Hadamard product, respectively. Based on the HDM, the image reconstruction is realized through the 2-D fast iterative shrinkage thresholding algorithm (ISTA), in which the gradient is derived by exploiting the properties of the tensor and matrix trace. The fast Fourier transform (FFT) and the nonuniform FFT are implemented to accelerate the computation. Due to good accommodation of the RCM and the LS-SAR sampling characteristics, the proposed method can work well for various PRI variation modes and scenario types. Simulations using the point scatter and the distributed target with wide-swath extension demonstrate the effectiveness as well as the robustness of the proposed method.

Clutter suppression in synthetic aperture radar (SAR) is one of the urgent and attractive problems in ground moving target indication (GMTI) application. With separate transmitter and receiver, ground clutter is nonstationary in bistatic forward-looking SAR (BFSAR), which directly leads to the inaccurate clutter covariance matrix (CCM) estimation. As a consequence, the traditional space-time adaptive processing (STAP) will suffer from a serious performance deterioration. In this article, an optimally matched space-time filtering (MSTF) technique is proposed to suppress nonstationary clutter for BFSAR systems. The main idea of the proposed method is to directly design and generate a suppression filter in space-time domain, whose space-time frequency response is matched with clutter spectrum. To construct the matched space-time filter, clutter modeling with arbitrary BFSAR configuration is first proposed to acquire space-time information of clutter spectrum. And then, the suppression filter can be designed and the design process is transferred into a constrained optimization problem (COP), according to the obtained clutter space-time information. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the COP and obtain the optimal solution, i.e., the desired matched space-time filter weight, for BFSAR nonstationary clutter suppression. Since the generation of the designed filter circumvents CCM estimation, the proposed method will not be affected by the nonstationary characteristic of BFSAR clutter. In October 2020, the first airborne BFSAR-GMTI experiment in the world has been successfully conducted by us, and the experimental results are given to validate the effectiveness of the proposed method.

The 2-D space variance of echo signals is the key problem of image processing for bistatic synthetic aperture radar (BiSAR) under nonlinear platform trajectories. Although there are some existing studies that present solutions to deal with space variance under linear or low-order motion, this problem is more severe when the trajectories are more complicated and the imaging scene sizes are larger. To deal with this challenging problem, this article proposes a new imaging method based on a novel azimuth-regularized wavenumber mapping and a highly efficient spatially variable phase filter. The novel wavenumber mapping as the major novelty of the method can simultaneously realize range–azimuth decoupling and coarse focusing in the space domain for all the targets. What is more, the phase function of the echo signal in the wavenumber domain is analytically expressed as a binary polynomial by deriving the inverse mapping of the wavenumber with respect to range frequency and azimuth time. Then, the spatially variable filter is designed based on the analytical expression and realized by upgrading the parameters along the azimuth direction. The filtering process has low complexity and can be executed in parallel for every cross-azimuth cell. Moreover, an algorithm for constraining the residual phase error is presented to guarantee both the efficiency and the accuracy of the image processing. Verified by numerical simulations, the proposed imaging method achieves a superior focusing effect than the existing method that is designed for BiSAR with highly maneuvering platforms while having lower computational complexity.

The unmanned aerial vehicle (UAV) is a low-cost and high-efficiency lightweight synthetic aperture radar (SAR)-mounted platform that can be used for a variety of military and civilian missions. Using multiple UAVs to form a swarm can break through the limitations of a single platform and has broad application prospects. In this article, swarm UAV SAR that contains tens or hundreds of UAV platforms is proposed for the first time. The concept and advantages of swarm UAV SAR are investigated, and the mission outlook is given. Afterward, the swarm UAV 3-D linear array SAR (LASAR) is illustrated, which enables high-resolution 3-D imaging in a single flight. Since the antenna array of the swarm UAV 3-D LASAR is sparse, the compressed sensing (CS) algorithm is applied, whose reconstruction performance is closely related to the correlation coefficient of the sensing matrix. Hence, the signal model of swarm UAV 3-D LASAR is derived, and the expression of the sensing matrix is deduced. The sensing matrix design in this article aims at obtaining satisfactory reconstruction performance by optimizing the distribution of the antenna elements, which directly influences the correlation coefficient of the sensing matrix. Considering the limitation of the practical conditions, the sensing matrix design problem is modeled as a constrained integer programming problem. Finally, a sensing matrix design method based on discrete constrained differential evolution (DCDE) algorithm is proposed to solve the optimization problem. Experimental results demonstrate the effectiveness and superiority of the proposed method.

Due to the division of the transceiver, a bistatic synthetic aperture radar (SAR) gains many advantages, such as forward-looking imaging and powerful anti-interference capabilities. In the meantime, information sharing (e.g., positions and status) between the transmitter and the receiver is required for SAR imaging. This article addresses the co- use waveform design for SAR-dual-functional radar communication (SAR-DFRC). To this end, we embed information into a time-frequency spectrum of the phase coded signal, which can be a feasible solution to SAR-DFRC, bringing the possibility of realizing a light-weighted, miniaturized, low-costed, spectrum reusable, and more confidential system. A novel time-frequency spectrum shaping (TFSS) SAR-DFRC architecture based on short-time Fourier transform (STFT) is proposed for the first time. The weighted peak sidelobe level (WPSL) is considered a figure of merit for SAR imaging performance. Information is embedded by nulling the time-frequency spectrum of the waveform. Here, we develop the majorization-minimization PSL-TFSS (MMPSL-TFSS) algorithm to solve the resulting nonconvex NP-hard optimization problem. The designed waveform can ensure imaging performance and obtain high communication capacity in the meantime. Experimental and numerical results highlight the effectiveness of the proposed SAR-DFRC framework for imaging performance and the secure transition to communication information.

Multistatic synthetic aperture radar (SAR) has the characteristics of all-day, all-weather and high-resolution imaging. It can observe a target from different directions simultaneously to obtain multi-angle observation information. However, jamming signals can affect multistatic SAR. When multiple range-deception jammers exist in the environment, multiple false targets are generated in the multistatic SAR image simultaneously, which can impact the readability of the information contained in multistatic SAR images. The locations of false targets are related to the configuration of multistatic SAR, it provides the potential for jamming suppression by adjusting the configuration. Thus, in this paper, we propose a jammer localization and jamming suppression method for multistatic SAR in a multi-jammer environment via configuration optimization. Firstly, a target detection algorithm and a discriminant algorithm are combined to detect and identify false targets. Then, the distribution law of false targets is analyzed, and false targets are classified into two categories according to the types of jammers. Combined with the multistatic SAR configuration and false target information, localization methods for range-deception jammers with different time delays are proposed. Finally, we model the configuration optimization problem as a multi-objective optimization problem (MOP), and the nondominated sorting genetic algorithm II is employed to solve the MOP. As a result, the configuration distribution of multistatic SAR can be altered to exclude false targets from the region of interest, thereby obtaining a multistatic SAR image without false targets. Simulation results demonstrate that the proposed method is effective.

Multistatic SAR is able to observe targets from different angles simultaneously, which enhances the information acquiring capability. However, multistatic SAR can still be affected by electromagnetic jamming, resulting in the misinterpretation of multistatic SAR images. This article proposes a method to locate multiple range-deception jammers and suppress jamming signals. First, the echo model of multistatic SAR under a multijammer environment is established. Second, the detection of interested targets in multistatic SAR images can be achieved through visual saliency detection methods based on spectral residual. Third, location distribution features of false targets in multistatic SAR images are analyzed, and the Euclidean distance criteria are used to effectively distinguish false targets. Accurate localization is then achieved by combing multistatic SAR configuration information. Finally, using a linear constrained minimum variance beamforming algorithm to suppress jamming signals, multistatic SAR images without jamming signals can be obtained. Simulation results validate the effectiveness of the proposed method in this article.

Bistatic synthetic aperture radar (BiSAR) has received more and more attentions because of its forward-looking imaging capability and configuration flexibility. For BiSAR moving target imaging, its non-cooperative motion leads to unknown range cell migration (RCM) and additional phase modulation. Consequently, moving target imaging in BiSAR face two main challenges: 1) The unknown RCM correction and Doppler parameter estimation are tightly coupled. 2) The Doppler parameters of the extended moving target’s different scattering points are different, i.e., the Doppler parameters are spatially variant. To cope with these problems, an optimal polar format refocusing method for bistatic SAR moving target imaging is proposed. First, the main part of tight coupling and spatial variation effects caused by the BiSAR platforms are eliminated, while the moving target is two-dimensional (2-D) defocused and shifted. Then, we analyze the characteristics of two-dimensional defocused and shifted of the moving target in BiSAR, and give the analytical expressions. On this basis, a new bistatic polar format transformation is introduced, in which the degree of freedom of defocusing result is reduced from 2-D to only one-dimension. After that, the parameter estimation and refocusing issues are transformed into a constrained optimization problem (COP), and differential evolution (DE) is applied to solve the COP and obtain the refocusing results. Finally, considering the spatial variation of the extended moving target, the compensation processing is performed to relocate each scattering point. Numerical simulations verify the effectiveness of the proposed method.

This article extends the shadow tracking for video-synthetic aperture radar (SAR) from a single-target framework to a multitarget framework, which is crucial for SAR ground moving targets’ identification. Inspired by FairMOT, the multitarget tracking framework for SAR shadow tracking is improved by using the triplet attention (TriAtt) mechanism and the lightweight multiscale network. By employing the ability to fuse spatial and feature dimensions of TriAtt and combining the lightweight network optimized by multiscale encoder–decoder and dilated convolution, a fast multiscale feature extraction module (FMsFEM) embedded with TriAtt is proposed for better tracking efficiency and performance. Experiments on the Sandiego video-SAR dataset validate that the TriAtt mechanism can improve the tracking performance of deep layer aggregation (DLA)-34, DLA-18, and FMsFEM significantly. FMsFEM with embedded TriAtt outperforms the state-of-the-art network (FairMOT with backbones of DLA-34 and DLA-18) with much faster frame rates. The average frame rates of FMsFEM and FMsFEM-TriAtt reach 60.32 and 56.13 fps for datasets with an image size of$1088\times 608$, which are about three times higher than the frame rates of others.

Video synthetic aperture radar (SAR) have received more and more attention in recent years as it can provide continuous images of the observed area. However, the enormous data generated by the multi-frame images in video SAR brings big challenges to its transmission, storage and processing, especially for small unmanned aerial vehicle (UAV) platform. In this paper, we aim at proposing an efficient video formation method for SAR system with reduced data. The video formation problem is modelled as a joint low-rank and sparse tensors recovery problem. After that, this problem is solved by an efficient tensor recovery method based on alternating direction method of multiplier. Compared with frequency-domain or time-domain imaging methods, the amount of data samples used can be greatly reduced. Numerical simulations validate the effectiveness of the proposed method.

The echo signal of slow-moving target is usually submerged in clutter signal. As a consequence, ground moving target (GMT) separation is challenging because of the decreasing of detection performance. To solve this problem, this paper proposes a moving target detection method with single-channel for bistatic forward-looking SAR (BFSAR) which mainly contains three steps. First, range cell migration (RCM) is corrected by Keystone transform. Next, Extended Nonlinear Chirp Signal (NLCS) algorithm is applied to equalize the spatial variant Doppler parameters and subpress clutter spectrum broadening. At last, according to the difference of Doppler FM rate between GMT and the stationary clutter, the Short Time Fourier Transform (STFT) is devoted to separate GMT from the stationary clutter. The effectiveness of the detection method proposed in this paper has been demonstrated and illustrated by numerical simulations.

Synthetic Aperture Radar (SAR) image is highly useful in civilian and military fields, such as ship detection, maritime search and rescue. Considering the target detection from SAR image, we propose a SAR image reconstruction and target extraction method via low-rank and sparsity constrains from under-sampled data. Firstly, the low-rank and sparsity constrains are incorporated into the SAR image reconstruction model, and the objective function is established based on Robust Principal Component Analysis (RPCA) theory. Then, the Augment Lagrange Multiplier (ALM) algorithm is used to transform this objective function to a convex optimization problem. Lastly, SAR image reconstruction and target extraction are obtained by Alternating Direction Method of Multipliers (ADMM) algorithm. The simulations are conducted to verify the effectiveness of the proposed method.

Traditional synthetic aperture radar (SAR) imaging methods focus on high-resolution imaging of the observation scene. In the application of specific target imaging and detection, such as threat target search, the priori knowledge of target can be used for better task performance. Therefore, it is highly desirable to improve the image quality of the target. In this paper, we propose a SAR formation method based on the sparse dictionary. Firstly, the sparse dictionary is learned through the SAR images of a specific target via K-SVD method. Then the SAR formation model is established as a sparse reconstruction problem by incorporating the sparse dictionary. Lastly, the problem is solved via Augment Lagrange Multiplier (ALM) method and Alternating Direction Method of Multipliers (ADMM) method. The proposed method is validated by the simulation, and the results show that the proposed method can effectively reconstruct the target.

The survivability and stability of synthetic aperture radar (SAR) in the increasingly severe electromagnetic environment are widely concerned. With the development of interception receiver technology, it is very difficult to prevent the competitor from detecting the radio frequency (RF) energy of radar. Therefore, more complex intra pulse modulation waveform is needed to prevent the competitor from effectively acquiring and analyzing information. In this paper, a novel low probability of intercept (LPI) waveform optimization framework is proposed. Symmetric piecewise linear functions (PWL) is used to define the waveform search space and a constrained multi-objective evolutionary algorithm is employed to solve the waveform optimization problem. Simulation results show that the proposed waveform has good performance of low interception and auto-correlation, which is suitable for SAR imaging.

In order to overcome layover and shadowing effects, three-dimentional(3-D) synthetic aperture radar(SAR) systems have developed rapidly. One of them is linear array SAR(LASAR). Therefore, the image result may defocus due to the antenna phase centers(APCs) motion measurement error. Thus a novel LASAR motion compensation approach is proposed. Based on the analysis that linear array motion error is approximately linear, the method estimates only selected APCs' phase error and derive the whole array's phase error via extrapolation. The compensation scheme is verified with simulations.

Video Synthetic Aperture Radar (SAR) has received extensive attention in recent years due to its continuous imaging capabilities. Video SAR requires continuous image reconstruction, and there are many redundant operations in the reconstruction process. In order to meet the real-time requirements of video SAR and improve data utilization, we propose a subaperture imaging algorithm based on PFA. First, the echo is divided into multiple subapertures, and coarse focusing is achieved respectively. Then the subaperture echo is subjected to wavenumber mapping to obtain high-resolution high-frame image output. All subaperture data has only been coarsely focused once, and interpolation is not required for wavenumber mapping, which improves the efficiency of image output. Finally, we use experimental data to verify the effectiveness of the algorithm.

This paper unrolls some preliminary results of a spaceborne-airborne bistatic SAR experiment, conducted in October, 2020 in Zhejiang, China, using GF-3 SAR satellite as the transmitter. Some important aspects of the experiment are firstly introduced, including bistatic acquisition geometry, receiving system, signal synchronization and theoretical spatial resolution. Then, the imaging processing flow is given, with emphasis on the data synchronization. A modified BP imaging method is proposed, which is suitable for direct signal synchronization scheme commonly used in spaceborne-airborne experiments. Finally, the imaging result is given with evaluation of the spatial resolution.

Geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO-SA-BiSAR) consists of GEO transmitter and airborne receiver, which has extensive application prospects in both civilian and military fields. However, the Doppler bandwidth in this configuration exceeds the transmitted pulse repeat frequency (PRF), which leads to sub-Nyquist sampling. To solve this problem, multi-receiving technique has been applied to the receiver to increase the equivalent sampling rate and reconstruct unambiguous image. In this paper, we take a different approach to recover the unambiguous image for GEO-SA-BiSAR with less receiving channels. The GEO-SA-BiSAR imaging problem is modeled as a problem of joint sparse and low-rank matrices recovery. To reduce the computing time of the traditional recovery method, a modified alternating direction method of multipliers (M-ADMM) is proposed, where the computing and storage of the computational expensive observation matrix is avoided. Simulation results reveal that the proposed method can recover the original image scene with high computational efficiency.

Unmanned ariel vehicle (UAV) can provide superior flexibility and cost-efficiency for modern radar imaging systems, which is an ideal platform for advanced remote sensing applications. In this paper, an energy-efficient passive UAV SAR system is proposed and investigated. The UAV platform passively reuses the backscattered signal from an external illuminator, such as SAR satellite, GNSS or ground-based stationary commercial illuminators, and achieves data communication and bi-static SAR imaging at a ground processing station. The mission concept and system block diagram are first presented with justifications on the advantages of the system. A set of mission performance evaluators is established to quantitatively assess the capability of the system in a comprehensive manner, including UAV navigation, passive SAR imaging and data communication. Finally, the validity of the proposed performance evaluators are verified by numerical simulations.

The clutter background poses a challenge to the detection and recognition of targets from synthetic aperture radar (SAR) images, especially when the target is submerged by the clutter. In this paper, we propose a target-oriented SAR waveform optimization method to deal with this problem. The proposed method constructs an optimization criterion jointing the signal-to-clutter ratio (SCR) and the resolution of transmitted waveform. Based on the prior information of the frequency response of the interested target, the clutter suppression performance and the range resolution performance are jointly optimized. The simulation results show that the proposed method can effectively improve the SCR of SAR image in the condition of low SCR, while the resolution performance is guaranteed.

Multistatic SAR can obtain information from different angles simultaneously, it has application potential in many fields. However, it still be affected by jammers. When deceptive jammers jamming the SAR system, the false target is generated in the SAR image. In this paper, when the false target is generated on the sea by jammer which located at the coast, the anti-jamming method is proposed. Firstly, echo model of multistatic SAR in deceptive jamming environment is established. Then, false targets are detected and recognized. Next, the jammer localization method is proposed according to relationship between the jammer and false targets. Finally, the jammer location is obtained and jamming signal suppression is achieved. Simulation results validate the effectiveness of the proposed method in this paper.

Miniaturized radar near-field imaging systems have become a hot spot for SAR imaging in recent years. The irregular synthetic aperture caused by arbitrary motion of miniaturized system is an important factor affecting imaging quality. On the other hand, the design of fast and high resolution 3D imaging methods is also a major issue. In this paper, a fast time-frequency joint three-dimensional imaging algorithm based on aperture linearization is proposed. First, irregular synthetic aperture is adjusted through special linearization method, and then the target is quickly and accurately imaged. Through formula derivation and simulation test analysis, the results show that the proposed method is feasible and maneuverable, which can solve the current problems.

Synthetic aperture radar (SAR) imaging has always been an area of concern and exploration by researchers. The restoration of elevation information is a core issue of SAR imaging. Therefore, this paper proposes a method of three dimensional (3D) surface reconstruction with multistatic SAR. The presence of elevation information makes the target shift in the SAR image which is related to the configuration of SAR. This method utilizes the range history of the scattering point, combining the quantitative relationship between the receivers and the transmitters, to realize the reconstruction of the three-dimensional surface. Simulations prove the effectiveness of the proposed algorithm.

A polar format algorithm (PFA) as a high-efficient imaging method is widely used in bistatic spotlight synthetic aperture radar (SAR). In cases where motion error causes great defocusing in PFA images, phase gradient autofocus (PGA) is suitable for motion error compensation due to its robustness. However, a significant coupling of range-azimuth directions in bistatic SAR (BiSAR) makes conventional PGA unapplicable in PFA images. For this limitation, this letter gives the solution to the adaptability of PGA in BiSAR based on the azimuth migration correction in the wavenumber domain. The bistatic motion error is modeled, and the caused phase error is derived and analyzed. Through the wavenumber-domain analysis, the 2-D coupling is indicated still existing after polar format imaging, unlike other algorithms that utilize range cell migration correction (RCMC). Then, a phase compensation for the migration of azimuth direction is proposed to remove the 2-D coupling. Based on this, the new autofocus arithmetic flow for bistatic PFA is designed. The effectiveness of the proposed method is verified by numerical simulations.

Low-earth-orbit (LEO) synthetic aperture radar (SAR) can achieve advanced remote sensing applications benefiting from the large beam coverage and long duration time of interested area provided by a geosynchronous (GEO) SAR illuminator. In addition, the receiving LEO SAR system is also cost-effective because the transmitting module can be omitted. In this article, an imaging method for GEO-LEO bistatic SAR (BiSAR) is proposed. First, the propagation delay characteristics of GEO-LEO BiSAR are studied. It is found that the traditional “stop-and-go” propagation delay assumption is not appropriate due to the long transmitting path and high speed of the LEO SAR receiver. Then, an improved propagation delay model and the corresponding range model for GEO-LEO BiSAR are established to lay the foundation of accurate imaging. After analyzing the sampling characteristics of GEO-LEO BiSAR, it is found that only 12.5% sampling data can be acquired in the azimuth direction. To handle the serious sub-Nyquist sampling problem and achieve good focusing results, an imaging method combined with multireceiving technique and compressed sensing is proposed. The multireceiving observation model is first obtained based on the inverse process of a nonlinear chirp-scaling imaging method, which can handle 2-D space-variant echo. Following that, the imaging problem of GEO-LEO BiSAR is converted to an L1regularization problem. Finally, an effective recovery method named complex approximate message passing (CAMP) is applied to obtain the final nonambiguous image. Simulation results show that the proposed method can suppress eight times Doppler ambiguity and obtain the well-focused image with three receiving channels. With the proposed method, the number of required receiving channels can be greatly reduced.

In synthetic aperture radar (SAR) imaging, moving target is generally mixed with stationary targets. Meanwhile, the image of a moving target is distorted and displaced due to the lack of its prior velocity information. Furthermore, imaging of a moving target for geosynchronous (GEO) spaceborne-airborne bistatic SAR (GEO SA-BiSAR) is a more challenging problem because the echo is sub-Nyquist sampled in azimuth. In this article, a simultaneous moving and stationary target imaging method for GEO SA-BiSAR is proposed. First, range models and the corresponding echo models of moving and stationary targets are established. The observation models for both moving and stationary targets with two receiving channels are derived based on the inverse of an efficient imaging algorithm. After that, the imaging problem of moving and stationary targets is modeled as a joint velocity estimation and sparse decomposition problem, which aims at optimizing the entropy of the moving target image and residual error of the formed images at the same time. Finally, a joint optimization method based on the particle swarm optimization (PSO) method and alternating direction method of multipliers (ADMM) is applied to achieve the imaging of moving and stationary targets and estimation of the moving target velocity. With two receiving channels, the accurate separation and focusing of stationary and moving targets as well as the precise estimation of moving target velocity can be achieved with sub-Nyquist sampling echo. Simulation results are presented to validate the effectiveness of the proposed method.

Geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO-SA-BiSAR) can achieve high-resolution Earth observation with superior system flexibility and efficiency, which offers huge potential for advanced SAR applications. In this article, the echo characteristics of GEO-SA-BiSAR are analyzed in detail, including range history, the Doppler parameters, and spatial variance. The distinct features of GEO-SAR and airborne receiver result in the failure of the traditional bistatic SAR range model and imaging methods. In order to deal with these problems and achieve high-precision data focusing on GEO-SA-BiSAR, this article first proposes a novel range model based on one-stationary equivalence (RMOSE) to accommodate the distinctiveness of the GEO-SA-BiSAR echo, which changes with orbit positions of GEO transmitter. Then, a 2-D frequency-domain imaging algorithm is put forward based on RMOSE, which solves the problem of the 2-D spatial variance of GEO-SA-BiSAR. Finally, simulations are presented to demonstrate the effectiveness of the proposed range model and algorithm.

Bistatic forward-looking spotlight synthetic aperture radar with stationary transmitter and maneuvering receiver (STMR-BFSSAR) is a promising sensor for various applications, such as the automatic navigation and landing of maneuvering vehicles. Because of the bistatic forward-looking configuration and the receiver's maneuvers, conventional image formation algorithms suffer from high computational complexity or small size of a well-focused scene if applied to STMR-BFSSAR. In this article, we propose a wavenumber-domain algorithm for STMR-BFSSAR image formation, which is termed the bistatic-range-Doppler-aperture wavenumber algorithm (BDWA). First, a novel range model in bistatic-range and Doppler-aperture coordinate space instead of conventional Cartesian coordinate space is established by employing the elliptic polar coordinate system and the method of series reversion. The novel range model not only makes the echo's samples to be regular along the direction of the bistatic-range wavenumber axis but also constructs a curved wavefront close to the true wavefront. Second, an operation termed wavenumber-domain gridding is conceived to regularize the echo's samples along the Doppler-aperture wavenumber axis, which can be implemented by 1-D interpolation. The proposed algorithm significantly outperforms the conventional algorithms in terms of computational complexity and scene size limits. Both point and distributed targets are simulated for two STMR-BFSSAR systems with different parameters. The simulation results verify the validity and superiority of the proposed BDWA.

Due to its ability of forming continuous images for a ground scene of interest, the video synthetic aperture radar (SAR) has been studied in recent years. However, as video SAR needs to reconstruct many frames, the data are of enormous amount and the imaging process is of large computational cost, which limits its applications. In this article, we exploit the redundancy property of multiframe video SAR data, which can be modeled as low-rank tensor, and formulate the video SAR imaging process as a low-rank tensor recovery problem, which is solved by an efficient alternating minimization method. We empirically compare the proposed method with several state-of-the-art video SAR imaging algorithms, including the fast back-projection (FBP) method and the compressed sensing (CS)-based method. Experiments on both simulated and real data show that the proposed low-rank tensor-based method requires significantly less amount of data samples while achieving similar or better imaging performance.

An efficient echo simulation algorithm considering topography in time domain for bistatic SAR is proposed in this paper. By using subaperture processing and equivalent scatter, this method can implement efficient echo simulation. At last, the effectiveness of this method is verified by numerical simulation.

This paper presents a long-time integration method for passive radar detection of marine target with multi-stage motions using global navigation satellite system (GNSS) as illuminator. Owing to the complex motion, the range cell migration (RCM) and the Doppler parameters of the target echo pertaining the multiple motion stages are different. To solve the problems, in this method, first the range symmetry transform (RST) is applied to correct the RCM over the multiple stages, after which a one-dimensional azimuth signal can be directly extracted from the two-dimensional echo. Then, the conjugate integration processing (CIP) is performed to accumulate the target energy during each stage into Doppler centroid (DC) and Doppler frequency rate (DFR) domain. Finally, in order to settle the difference in Doppler parameters between multiple stages, the DC-DFR maps are projected and combined in high-dimensional Doppler parameter domain after Doppler frequency shift compensation, implementing the well integration of the echo over the entire long time. The effectiveness of the proposed integration method is demonstrated by simulations.

For a non-cooperative ground moving target (GMT), its complex motion inevitably induces unknown range cell migration (RCM) and Doppler frequency migration (DFM) in Bistatic SAR (BiSAR) echo. Unfortunately, the existence of RCM and DFM often results in a deteriorative or even unacceptable performance on target detection and parameter estimation. In this paper, a novel coherent integration approach for BiS-AR GMT detection and parameter estimation based on two-dimensional (2-D) deramp processing is proposed. First, the deramp processing along the range frequency is exploited for GMT's unknown RCM correction. Then, the second deramp function along the slow time is constructed to reduce the order of azimuth phase and eliminate the effect of DFM. Finally, GMT's energy can be accumulated into a peak and Doppler parameter can be obtained via azimuth fast Fourier transform (FFT) and range inverse FFT. The proposed approach is computationally efficient, since it can be implemented only by complex multiplications and FFT. Simulations are given to verify the effectiveness of the proposed approach.

In this paper, a weighted dense connected convolutional network(WDCCN) is proposed for SAR image super-resolution. In the network, to enhance feature propagation and the super-resolution performance, each layer will receive the output from all the previous layers in a different weight proportion. At last, the experimental results indicate that weighted dense connected convolutional network can realize SAR image super-resolution well.

While having achieved great success in rich real-life applications, deep neural network (DNN) models have long been criticized for their vulnerability to adversarial attacks. Tremendous research efforts have been dedicated to mitigating the threats of adversarial attacks, but the essential trait of adversarial examples is not yet clear, and most existing methods are yet vulnerable to hybrid attacks and suffer from counterattacks. In light of this, in this paper, we first reveal a gradient-based correlation between sensitivity analysis-based DNN interpreters and the generation process of adversarial examples, which indicates the Achilles's heel of adversarial attacks and sheds light on linking together the two long-standing challenges of DNN: fragility and unexplainability. We then propose an interpreter-based ensemble framework called X-Ensemble for robust adversary defense. X-Ensemble adopts a novel detection-rectification process and features in building multiple sub-detectors and a rectifier upon various types of interpretation information toward target classifiers. Moreover, X-Ensemble employs the Random Forests (RF) model to combine sub-detectors into an ensemble detector for adversarial hybrid attacks defense. The non-differentiable property of RF further makes it a precious choice against the counterattack of adversaries. Extensive experiments under various types of state-of-the-art attacks and diverse attack scenarios demonstrate the advantages of X-Ensemble to competitive baseline methods.

Echoes of bistatic forward-looking synthetic aperture radar (BFSAR) disperse to multiple range cells and exist spatial frequency extension as well as Doppler spectrum extension (i.e., namely space-time extension). Furthermore, the characteristics of BFSAR clutter are strongly nonstationary and spatial variants. Because of the abovementioned issues, clutter and moving targets are fully overlapped in the initial 3-D space-time-range raw data domain, and the clutter cannot be suppressed effectively. To solve this problem, a BFSAR multipulse displaced phase center antenna (MP-DPCA) method is proposed in this article. First, keystone transform without Doppler ambiguity is applied to remove the coupling between the space-time and range domains. Hence, the overall complex 3-D processing in the space-time-range domain is reduced to independent 2-D processing in each space-time domain. Subsequently, a spatial-dechirp processing is applied in the space-time domain to eliminate the spatial frequency extension. Meanwhile, Doppler parameters of clutter point scatterers are equalized by nonlinear chirp scaling processing in the frequency and time domains. Accordingly, the Doppler spectrum extension of point scatterers can be eliminated by a uniform azimuth dechirp processing. After aforesaid three steps, the clutter and moving targets are separated in the space-time domain. Finally, based on the linear phase difference of clutter between the channels, a multipulse canceller can be designed to suppress the clutter. Compared with the existing space-time adaptive procession (STAP) and DPCA methods, this method not only overcomes the nonstationary problem in BFSAR but also conquers the strict application conditions of DPCA. Simulation results are given to verify the effectiveness of the proposed method.

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models.

This paper briefly introduces the basic scheme of a maritime moving target detection and localization method with multiple BeiDou satellites as transmitters. To detect the target and localize it, a receiver local-coordinates is constituted, whose axes are X, Y, and Doppler frequency rate (DFR), and the received echoes from multiple satellites are integrated into the local-coordinates. In this method, first symmetrical keystone transform (SKT) is applied to correct range cell migration (RCM) and set Doppler centroid (DC) to zero. Then, dechirp-FFT is applied to integrate each echo in fast-time and DFR domain. Finally, each DFR integration result is back projected to local-coordinates according to its bistatic range. From the final integration result, target can be detected and localized simultaneously. The effectiveness of the proposed method is demonstrated by simulations.

Time and frequency synchronization is a crucial problem for Bistatic SAR systems. In this paper, frequency reference error of bistatic SAR system is analyzed firstly. Then the time and frequency synchronization errors are deduced and the intrinsic relationship between them is revealed. At last the effect of the frequency reference error is studied. Simulation and experiment results are given to verify the effectiveness of the analysis.

This paper proposes a clutter suppression method for the bistatic forward-looking SAR(BFSAR). The proposed method relies on a proper processing of the received echoes. First, keystone transform (KT) and the higher-order RCM correction are applied to correct the range cell migration (RCM). Then, azimuth nonlinear chirp scaling (NLCS) and azimuth dechirp are applied to suppress the spatial-variant Doppler spectrum extension. After the aforesaid two steps, the moving targets and clutter can be separated in the space-time domain, and the clutter signal with different pulses contains only one constant phase difference. Thus, the two-pulse clutter canceller can be designed to suppress the clutter. Finally, it is suggested that this proposed method can improve the signal to clutter ratio (SCR) and are beneficial for the subsequent signal processing, e.g. target detection. Simulation results are provided to validate the effectiveness of the clutter cancellation method.

This paper proposes a parking space information monitoring system by millimeter wave synthetic aperture radar (SAR) based on unmanned aerial vehicle (UAV). Parking space information that people are concerned about includes vacant parking place, parking place occupied by obstacles and place parked by vehicles. Specially, the free parking space detection is an important module for the parking guidance system (PGS) that can help drivers to find parking space efficiently. In this system, we obtain high resolution SAR images of parking lots at first. Then, in order to define the free parking space, Maximally Stable Extremal Region (MSER) method is exploited to leach the candidate regions occupied by vehicles from millimeter wave SAR images. Next, the system utilize visual saliency detection method to extract obstacles from the non-parked parking space acquired by pre-detection. Ultimately, the three types of information have been determined, including vacant parking space, parking space occupied by obstacles and the parked place. Experimental results prove that the integrated scheme performs well in parking information determination.

Resolution features of spotlight SAR appear space-variant in most cases, however different in PFA. In this paper, we make analysis on the resolution features of PFA and BPA through wavenumber spectrum and draw some novel conclusions. Distinguishing from BPA, the range-Doppler contours appear parallel in the imaging domain of PFA, which makes the space-variation of the resolution features disappear. As verified by simulations, these unique phenomenon will be useful in further research on spotlight SAR.

Finding a suitable parking position often leads to much traffic pressure and time consumption in a busy parking lot. An auxiliary parking method based on automotive millimeter wave SAR is proposed in this paper. Firstly, Maximally Stable Extremal Region (MSER) method is utilized to extract the candidate regions occupied by parked vehicles from the millimeter wave SAR images. Then, in order to eliminate the false alarm candidate regions, we employ the morphological filter and utilize the centroid position to further refine the candidate regions. Thirdly, the difference in width-to-height ratio of the candidate regions is exploited to distinguish the parking directions of the cars. After that, the available parking spaces are located according to the parking direction. Finally, further remove the spaces occupied by obstacles, and plan reasonable parking routes. Experimental results based on measured data show that the proposed method has outstanding detection and parking route planning performance in different scenes.

Multistatic synthetic aperture radar (SAR) have achieved significant performance in the field of anti-jamming for its flexible configuration. Hence, its capacity of anti-jamming is very important in electronic warfare. In addition, it is difficult to remove the false targets caused by the deceptive jammer from SAR echoes. We propose a novel anti-deceptive jamming method for multistatic SAR in this paper which is able to locate the deceptive jammer and eliminate its influence. We first apply maximally stable extremal region (MSER) and European-distances-based method to detect the false targets. The position of the jammer can then be obtained by exploiting the position information of the false targets and the SAR transmitters/receivers. At last, beamforming is applied to achieve the anti-deceptive jamming. Experimental results demonstrate the effectiveness of our proposed method.

SAR ship detection is essential for marine monitoring. Due to the high similarity between the harbor and the ship body on gray and texture features, the traditional methods are unable to achieve effective inshore ship detection. An improved Faster R-CNN based on MSER decision criterion for SAR ship detection in harbor is proposed in this paper. It is a ship detection method based on the combination of feature-based method and pixel-based method. Firstly, Faster R-CNN is used to generate region proposals. Then, replace the threshold decision criterion of Faster R-CNN with the maximum stability extremal region (MSER) method to reassess the generated region proposals with higher scores, aiming at improving the detection rate and reducing the false alarm rate simultaneously. Experimental results based on satellite-borne SAR data illustrate that the proposed method obtains excellent detection performance and low false alarm rate.

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In BFSAR, the compensation of the spatially variant motion errors is of great significance to get a well-focused image. In this paper, a motion compensation method based on keystone transform and modified autofocus back-projection is presented to deal with this problem. Keystone transform is applied to remove the spatial variation of range cell migration (RCM) and the first-order term of RCM errors simultaneously, prepares for the following modified autofocus back-projection, which can eliminate the high-order term of azimuth phase errors. Simulation results verify the validity and efficiency of the presented method.

The clear contour is required when realize azimuth superresolution of forward-looking radar imaging in many applications. Traditional deconvolution methods achieve the azimuth superresolution but are limited in contour recovery. Although the total variation (TV) method can be used to keep the contour information, it's sensitive to noise because of derivation. In this paper, we propose an improved total variation (ITV) method to realize azimuth superresolution of forward-looking radar imaging and recover the contour information. Firstly, the TV norm and L2norm are combined as the penalties under regularization framework. Then the regularization problem is solved by split Bregman algorithm. The proposed ITV method achieves higher azimuth resolution and better contour recovery performance than traditional methods, and the super performance is verified by simulations lastly.

Stochastic radiation radar (SRR) systems allow for the forming of radar images by transmitting stochastic signals to form the stochastic radiation field and thereby increase the target observation information to achieve high resolution imaging. In this paper, we examine the use of the online SParse Iterative Covariance-based Estimation (SPICE) algorithm to suppress the noise and improve the operational efficiency. The SPICE algorithm is based on a weighted covariance fitting criterion, and has recently been generalized to allow for an improved reconstruction performance. The used online extension can take advantage of echoes non-correlation along time, allowing for updating the imaging result through successive echo sequences. The simulation results verify the superior performance of the resulting estimator as compared to other recent SRR imaging methods.

Coastal surveillance has long been paid a lot of attention for the threat of flooding due to some natural phenomena, such as global warming. Prompt and accurate reaction to the visualization of the flooded areas is the key. An image fusion rule is thus proposed in this paper to achieve image enhancement of the flooded areas. The rule, targeted at high-frequency parts of the synthetic aperture radar (SAR) and optical images, is able to exploit and combine the merits of both SAR and optical images to obtain the exact flooded areas with the clear boundaries. Experimental results validate the performance of the proposed fusion rule and show that not only the clarity of fusion images is improved, but also the texture and brightness contrast are greatly enhanced.

Geosynchronous (GEO) spaceborne-airborne bistatic synthetic aperture radar (GEO-BiSAR), where the high-altitude transmitter provides continuous illumination for the receiver, is capable of providing benefits to remote sensing applications. However, obtaining a focused image with high efficiency is a challenging work. The severe 2-D space-variant range cell migration and Doppler modulation introduced by the two moving platforms make the echo hard to be focused. Moreover, the azimuth ambiguity due to the low pulse repetition frequency adopted by the GEO illuminator seriously deteriorates the quality of the final image. In order to simultaneously suppress the azimuth ambiguity and obtain well-focused images, a weighted fast factorized backprojection (FFBP) method is proposed for multichannel GEO BiSAR in this letter. First, the Doppler ambiguity of GEO BiSAR is analyzed and the azimuth multichannel receiving technique is introduced. Then, the multichannel transfer function for GEO BiSAR is derived. Based on the multichannel transfer function, a weighted FFBP method is proposed to achieve accurate focusing and ambiguity suppression. Finally, the simulation results validate the effectiveness of the proposed method.

An echo model plays a pivotal role in the processing of synthetic aperture radar (SAR) data. It has been shown that the widely adopted echo models based on stop-and-go approximation (SAG) are unsuitable for use in many emerging radar systems with high resolution or high-speed platforms. More accurate echo models without SAG have been proposed for some special monostatic scenarios, such as low-Earth-orbit SAR and geosynchronous SAR. However, due to their inherent assumptions, such as linear and constant-speed/orbit-based motion, these conventional echo models cannot be applied to general cases, which involve bistatic configurations and maneuvers. Maneuvers or nonlinear/inconstant-speed motion are very common in cases, such as circular SAR and maneuvering-platform-borne SAR. In this letter, we propose a generalized echo model for bistatic SAR, which accounts for maneuvers of the platforms during pulse propagation. Moreover, we obtain a high-precision closed-form expression of the model by approximating high-order terms of the accurate range history of the receiver. Compared with the conventional models, the proposed echo model can be applied to a much greater number of cases, ranging from slow and constant-speed platforms to fast and maneuvering platforms. Theoretical analysis and backprojection-based image formation simulations confirm the validity of the proposed echo model.

Due to the high-altitude illumination and the separation of the receiver and transmitter, geosynchronous (GEO) spaceborne-airborne bistatic synthetic aperture radar (BiSAR) is more flexible and accessible in remote sensing applications. In this paper, the Doppler characteristics of GEO BiSAR with a squint receiver are analyzed. It is found that the Doppler spectrum is generally aliased in GEO BiSAR regarding the low pulse repetition frequency (PRF) adopted by the GEO SAR. In order to suppress the ambiguity without adjusting the PRF of GEO SAR, the azimuth multichannel receiving technique is applied to the receiver and then the multichannel transfer function for GEO BiSAR is derived. However, the whole bandwidth of the imaging scene is much larger than that of the center point, which requires extra receiving channels to suppress the ambiguity and thereby increasing the system complexity. A two-step nonlinear chirp scaling (NLCS) method is proposed to obtain the well-focused image with reduced receiving channels. First, a preprocessing step is conducted to achieve space-variant range cell migration correction. After that, the first-step NLCS processing is applied to equalize the 2-D space-variant Doppler centroid and thereby the Doppler bandwidth is decreased, i.e., the required number of receiving channels for reconstruction is reduced. Then, the unambiguous spectrum is reconstructed based on the proposed multichannel transfer function. Finally, the second-step NLCS processing is carried out to equalize the 2-D space-variant high-order Doppler parameters and obtain the well-focused image. The simulation results validate the effectiveness of the proposed method. With the proposed two-step NLCS method, the well-focused image for GEO BiSAR is obtained and the required number of receiving channels can be decreased, which is beneficial to reducing the system complexity and hardware cost.

Due to its flexibility and low cost, the bistatic forward-looking synthetic aperture radar (BFSAR) which employs side-looking transmitter and forward-looking receiver has been studied in recent years. Sparsity-based techniques have been applied in the field of BFSAR imaging and show great potential. In sparsity-based BFSAR imaging, compensation of the motion errors is crucial to get a well-focused image. For fields that admit a sparse representation, we propose a sparsity-based imaging approach integrated with motion error estimation and compensation in this paper. First, a novel joint phase-amplitude compensation-based motion error correction scheme is developed to cope with the spatial variance of motion error. Then, an inversion observation model of the range-Doppler algorithm combined with motion error correction is derived, based on which a joint problem of BFSAR imaging and motion error estimation is formulated as a sparse recovery problem and solved in an iterative way, where in each iteration, both image formation and motion error correction are carried out. Experiments on both the simulated and real BFSAR data show that the proposed method can obtain a more accurate estimation result, and generate better focused images compared with the existing methods.

Back-projection (BP) is a reliable synthetic aperture radar (SAR) imaging algorithm because of its high-resolution and strong adaptability. However, it is hard to implement because of its high computational complexity. Fast factorized BP (FFBP) is a new way to fix this problem. Like traditional BP, FFBP is compatible with arbitrary flight paths if the track deviations are measured within fractions of a wavelength. However, when the motion information is not accurate enough, autofocus become an important way to get well-focused images. In this paper, we present an effective autofocus method for FFBP to solve the imaging problem caused by platform's motion errors. First, an image quality evaluation function with unknown phase error based on image sharpness for FFBP is established. Then, the phase error computation for autofocus is modeled as an optimization problem. Second, the coordinate descent (CD) and secant processing are introduced to the maximum image sharpness problem. The proposed method keeps the rapid imaging performance of FFBP and solves well the motion error compensation problem. In the end, simulated data and real data were used to verify the effectiveness of the proposed algorithm.

In geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO-BiSAR) system, the airborne platform achieves high-resolution imaging by passively receiving the signal from the interested scenario. In this paper, the Doppler characteristics of GEO-BiSAR and the individual contribution of the transmitter and the receiver are first analyzed. The airborne receiver is found to be the dominant contributor for the total Doppler bandwidth, which will lead to Doppler spectrum aliasing regarding the low pulse repetition frequency (PRF) adopted by the GEO-SAR. In order to suppress the Doppler ambiguity without adjusting the PRF of GEO-SAR, azimuth multichannel receiving technique is introduced to the airborne receiver. The multichannel transfer function is derived based on the method of series reversion and the spectrum reconstruction algorithm is then modified for multichannel GEO-BiSAR. Moreover, the reconstruction performance is closely related to the corresponding spacing between each channel (i.e., channel configuration). Therefore, the channel configuration design for GEO-BiSAR aims at optimizing the azimuth ambiguity-to-signal ratio with a satisfactory level of signal-to-noise ratio scaling factor by adjusting the channel configuration. The channel configuration design is modeled as a constrained single objective optimization problem (CSOP). Then, a channel configuration design method based on differential evolution and feasibility rule is proposed to solve the CSOP and obtain the channel configuration for the receiver with the optimal reconstruction performance. Finally, simulations results are presented to verify the effectiveness of the proposed method, and characteristics of channel configuration are analyzed in detail, which can be a practical guide for the implementation of multichannel GEO-BiSAR systems.

Being capable of providing weather-independent, day-and-night, forward-looking, and high-resolution images, bistatic forward-looking synthetic aperture radar (BFSAR) is a promising sensing technique in applications such as the scene-matching-aided navigation for recently emerging high-speed maneuvering platforms (HMPs). Because of the high speed and the great maneuverability of HMPs and the bistatic forward-looking configuration, conventional image formation algorithms, such as polar format algorithm (PFA), are no longer suitable for HMP-borne BFSAR (HMP-BFSAR). Hence, in this paper, we propose a novel PFA for HMP-BFSAR image formation. In the proposed PFA, a range model, termed as quasi-continuous -move range model, is established by taking the maneuvers of the receiver during pulse propagation into account instead of adopting stop-and-go approximation. Moreover, to take advantage of the collected k -set efficiently, an affine mapping, termed as k -set affine mapping, is conceived to transform the parallelogram-shaped k -set support region to a horizontal and quasi-rectangular one. Furthermore, to compensate for the defocus effect induced by wavefront curvature, a closed-form refocus filter based on the implicit function theorem is derived. Both point target simulation and distributed target simulation are presented in this paper. The simulation results show that the proposed PFA significantly outperforms the conventional PFA in terms of focusing quality and computational efficiency when applied to HMP-BFSAR image formation.

Due to the high altitude illuminator and the separation of the receivers and transmitter, Geosynchronous (GEO) spaceborne-airborne bistatic SAR (GEO BiSAR) is more flexible and accessible in remote sensing applications. However, by introducing a high speed airborne platform as receiver, azimuth spectrum aliasing occurs. In order to suppress the azimuth ambiguity without increasing the PRF of GEO SAR system, azimuth multichannel receiving technique is introduced to the airborne receiver in this paper. Firstly, the Doppler characteristics of GEO BiSAR are analyzed. Then, the multichannel transfer function for multichannel GEO BiSAR is derived and a modified multichannel reconstruction method is proposed to suppression the azimuth ambiguity. Finally, simulation results validate the effectiveness of the proposed method.

Geosynchronous (GEO) spaceborne-airborne multistatic SAR (GEO MulSAR) is flexible and accessible in remote sensing applications. Moreover, the information obtained by the multiple airborne receivers can be fused to enhance the spatial resolution. However, the fused spatial resolution significantly depends on the applied multistatic topology. In order to achieve the optimal fused spatial resolution by properly adjusting the imaging topology, a topology design method is proposed in this paper. Firstly, the spatial resolution model of GEO MulSAR is given and the dependance of the spatial resolution on the multistatic topology is analyzed. Then, a topology design method is proposed to obtain the best multistatic topology based on multiobjective optimization methods. Finally, the simulation results validate the effectiveness of the proposed method.

Moving targets are usually defocused in synthetic aperture radar (SAR) images due to across range unit (ARU) range migration and Doppler migration caused by unknown motion parameters. To eliminate the influence of these issues on moving target imaging, a novel Doppler parameters estimation method for moving targets is proposed. First, second-order keystone transform (SOKT) is applied to remove range curvature. Then, a deramp function is proposed to reduce the order of coupling and remove the residual first-order coupling. In the following, Doppler parameters can be obtained through range IFFT and azimuth FFT. The proposed method is capable of accumulating the energy of ARU target's trajectory into a peak and obtaining Doppler parameters in range-Doppler domain with low computational complexity. Furthermore, it has better antinoise performance comparing with the Hough transform and Radon transform. The effectiveness of the proposed method is validated by simulated data for ground moving targets.

Synthetic aperture radar and its application in remote sensing have been an international focus in recent years. Feature extraction is a key step in SAR automatic target recognition. In this paper, 2DLSDDP is proposed for dimensional reduction and feature extraction, which is based on manifold learning theory. 2DLSDDP preserves not only local and class information of the original dataset, but also intrinsic geometric information of the image. In addition, it seeks a proper clustering direction in the neighborhood for each sample in feature extraction, which enhances the discriminative capability of the method. Compared to other SAR imagery feature extraction methods, the experiments based on MSTAR database show that the proposed method improves the recognition performance.

Raw data generation of SAR echo is important for the evaluation of both imaging algorithms and SAR system design. Common time-domain echo generation methods (represented by projection method) are accurate for most of SAR modes but usually inefficient, while the existing FFT-based methods can only adapt to some relatively ideal configurations for bistatic SAR. Hence, this article will focus on FFT-based methods and try to solve the limitation of adaptability. For bistatic SAR with complicated configuration or maneuvering platform trajectories, 2-D spatial variation and difficulty on obtaining the 2-D spectrum of echo signal are the major problems to be faced with. To solve these problems, this article proposes an efficient raw data generation method based on 2-D inverse wavenumber mapping. This method first performs a 2-D FFT on the raw image to generate its 2-D wavenumber spectrum, then induces the space-variant phase using the 2-D inverse wavenumber mapping. Simulation results are presented to verify the validity of the proposed method.

As for the bistatic forward-looking synthetic aperture radar (BiForSAR) imaging, frequency-domain imaging algorithms integrated with autofocus is advisable to get a well focused BiForSAR image in the presence of uncompensated motion errors. Nevertheless, a severe drawback of the autofocus algorithms is that they are only capable of removing one-dimensional azimuth phase errors. In range direction, there exists nonsystematic range cell migration (NsRCM) originated from motion errors and RCM correction procedure in frequency domain imaging algorithms as well. In this paper, the causes of different NsRCM in BiForSAR imaging are analyzed, and the corresponding relationship between different NsRCM is deduced. Based on the analyses, we propose an autofocus NsRCM correction scheme for BiForSAR imagery using frequency domain imaging algorithms, being capable of eliminating the range-dependent NsRCM.

Despeckle is a key post-processing step for Synthetic Aperture Radar (SAR) images [1], because speckles severely affect visual quality and intelligent interpretation. However the existing despeckle methods have a problem in texture preservation when the speckles are reduced. To solve this problem, image heterogeneity measurement is utilized to improvement the non-local means (NLM) despeckle method in this paper. First, an anisotropic window is constructed using coefficient of variation (CV), which is used for heterogeneity measurement. The anisotropic window are used to replace the isotropic window for the similarity measurement in NLM method. Furthermore, for weight function, an adaptive filter parameter based on heterogeneity measurement is designed to adjust the despeckling degree for different regions. The simulation results show that the proposed method outperforms than the existing algorithms.

For high-quality synthetic aperture radar (SAR) processing, Doppler centroid estimation is an essential procedure that can help to solve the SAR platforms motion parameters conversely, construct azimuth matching function and is important for target location as well. In this paper, a Doppler centroid estimator based on phase center tracking is proposed for SAR. An evaluation function is designed to measure the estimated Dopper centroid accuracy. In order to improve the calculation accuracy and computational efficiency, the evaluation function is solved by binary stage optimization to estimate the Doppler centroid. Simulations validate the effectiveness of this method.

When the target has large size or the target moves irregularly, traditional inverse synthetic aperture radar (ISAR) imaging method will lead to a poor image quality due to space variate and migration of echo envelop. In this paper, a novel method based on Back Projection (BP) and Particle Swarm Optimization (PSO) is proposed, which can achieve high quality images under the circumstances of irregular motion, large target and low signal to noise ratio (SNR). First, Target motion is modeled as a turntable, then the translational motion and rotational motion are modeled as two polynomials. Entropy of coherent superposition value of part of the imaging scene pixels based on BP algorithm is utilized as the evaluation function to estimate the polynomial coefficients based on an optimization algorithm such as PSO. Once the polynomial coefficients are estimated, a high quality image of the whole scene can be obtained by BP algorithm. The simulation results verify the effectiveness of the proposed method.

Due to finite of information dimension of single bistatic synthetic aperture radar (SAR), to expand more information about ground objects we research multistatic SAR which can be decomposed into groups of bistatic SAR. It is known that scattering properties of the different viewing angles is different. In this paper, we focus on system of single transmitter and triple receivers and use polar format algorithm(PFA) to obtain images of ground objects for the triple receivers. A geometric distortion correction method is proposed due to elevation of ground objects. After the distortion correction, the three SAR images are registrated, then we put images into red, green, blue(RGB) channels respectively to realize fake color synthesis, and thus realize information fusion.

Spatial resolution plays a key role in evaluating the quality of synthetic aperture radar (SAR) images. Due to its flexible geometry and configuration, bistatic SAR (BSAR) shows much more complex resolution characteristics than mono static SAR, such as coexistence of non-square and nonorthogonality. Traditional comprehensive metrics of spatial resolution ignored the non-square property. SAR-based template matching is widely used in various fields such as scene-matching-aided navigation and its performance is influenced by the spatial resolution of the SAR image. In this paper, the non-square property of the resolution of BSAR is analysed by the resolution ellipse. Then the template matching method and the metric of performance are presented. Finally, template matching performance of BSAR systems with resolutions of different non-square degree but same resolution cell area is studied and simulated.

Bistatic forward-looking synthetic aperture radar (BFSAR) is a promising technique for applications such as automatic navigation and landing in all weather conditions and in day and night. Conventional echo models are no longer valid for the BFSAR mounted on the emerging hypersonic vehicle (HSV) due to its high-speed large-acceleration motion characteristics. In this paper, by taking the nonlinear maneuvers of the HSV receiver during pulse propagation into account, a closed-form non-stop-and-go echo model named maneuvering-during-pulse-propagation echo model is proposed for HSV-BFSAR. The model errors are analysed theoretically. The simulation verifies the validity of the proposed model.

Recent years have witnessed the unprecedented rising of time series from almost all kindes of academic and industrial fields. Various types of deep neural network models have been introduced to time series analysis, but the important frequency information is yet lack of effective modeling. In light of this, in this paper we propose a wavelet-based neural network structure called multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware deep learning models for time series analysis. mWDN preserves the advantage of multilevel discrete wavelet decomposition in frequency learning while enables the fine-tuning of all parameters under a deep neural network framework. Based on mWDN, we further propose two deep learning models called Residual Classification Flow (RCF) and multi-frequecy Long Short-Term Memory (mLSTM) for time series classification and forecasting, respectively. The two models take all or partial mWDN decomposed sub-series in different frequencies as input, and resort to the back propagation algorithm to learn all the parameters globally, which enables seamless embedding of wavelet-based frequency analysis into deep learning frameworks. Extensive experiments on 40 UCR datasets and a real-world user volume dataset demonstrate the excellent performance of our time series models based on mWDN. In particular, we propose an importance analysis method to mWDN based models, which successfully identifies those time-series elements and mWDN layers that are crucially important to time series analysis. This indeed indicates the interpretability advantage of mWDN, and can be viewed as an indepth exploration to interpretable deep learning.

Since the 21st century, the global outbreaks of infectious diseases such as SARS in 2003, H1N1 in 2009, and H7N9 in 2013, have become the critical threat to the public health and a hunting nightmare to the government. Understanding the propagation in large-scale metapopulations and predicting the future outbreaks thus become crucially important for epidemic control and prevention. In the literature, there have been a bulk of studies on modeling intra-city epidemic propagation but with the single population assumption (homogeneity). Some recent works on metapopulation propagation, however, focus on finding specific human mobility physical networks to approximate diseases transmission networks, whose generality to fit different diseases cannot be guaranteed. In this paper, we argue that the intra-city epidemic propagation should be modeled on a metapopulation base, and propose a two-step method for this purpose. The first step is to understand the propagation system by inferring the underlying disease infection network. To this end, we propose a novel network inference model called D 2 PRI, which reduces the individual network into a sub-population network without information loss, and incorporates the power-law distribution prior and data prior for better performance. The second step is to predict the disease propagation by extending the classic SIR model to a metapopulation SIR model that allows visitors transmission between any two sub-populations. The validity of our model is testified on a real-life clinical report data set about the airborne disease in the Shenzhen city, China. The D 2 PRI model with the extended SIR model exhibit superior performance in various tasks including network inference, infection prediction and outbreaks simulation.

Geosynchronous (GEO) spaceborne-airborne multistatic synthetic aperture radar (GEO MulSAR) is more flexible and accessible in remote sensing applications because of the high-altitude illuminator and the separation of the receivers and transmitter. In addition, the information obtained by the multiple airborne receivers can be fused to enhance the spatial resolution. However, the fused spatial resolution severely depends on the applied multistatic topology. To achieve the optimal fused spatial resolution by properly adjusting the imaging topology, a topology design method is proposed in this letter. First, the spatial resolution model of GEO MulSAR is given, and the dependence of the spatial resolution on the multistatic topology is analyzed in detail. Then, a topology design method is proposed to obtain the best multistatic topology that simultaneously optimizes the resolution cell area and resolution disequilibrium factor. Finally, the simulation results validate the effectiveness of the proposed method, and some insights into designing the multistatic topology are given.

In synthetic aperture radar (SAR) imagery, images of ground moving targets (GMTs) are smeared, distorted, and shifted. Current GMT imaging methods are mostly based on range-Doppler algorithms, which have two main drawbacks: 1) coupling between range cell migration correction (RCMC) and Doppler parameter estimation and 2) cross terms degrade the performance of the nonlinear estimation methods. In this paper, an optimal 2-D spectrum matching method for SAR GMT imaging is proposed. The main innovation or advantage of this method is that the GMT imaging problem is transformed into a constrained optimization problem, and differential evolution is applied to guarantee a high-processing efficiency. As they are associated with the Doppler centroid variation compensation and range shift compensation processing, all GMT point scatterers can be well focused and well located. Compared with the current methods, the improvements exhibited by this method include three main benefits: 1) RCMC and Doppler parameter estimation can be simultaneously accomplished; 2) both along- and cross-track GMT velocities can be simultaneously estimated; and 3) this method can be applied to both monostatic and bistatic SARs. Numerical simulations and experimental data processing have verified the effectiveness and robustness of the proposed method.

In general, autofocus methods integrated with frequency-domain imaging algorithms are instrumental to obtain a well-focused bistatic forward-looking synthetic aperture radar (BFSAR) image in the presence of motion errors. Nevertheless, before applying autofocus methods to correct the azimuth phase errors, range cell migration (RCM) should be eliminated by the RCM correction (RCMC) procedure in frequency-domain imaging algorithms. With motion errors being taken into account, there always exists some residual nonsystematic RCM (NsRCM), which refers to the residual migration components after the RCMC procedure. For the conventional side-looking SAR, NsRCM is caused by motion errors. On the other hand, NsRCM of BFSAR is originated from motion errors before RCMC and the NsRCM amplified by the RCMC procedure. In this paper, we analyze the different types of NsRCM in BFSAR imaging and their relationship. Based on the analyses, we propose an autofocus NsRCM correction scheme for BFSAR imagery using frequency-domain imaging algorithms that can eliminate the range-dependent NsRCM. The proposed scheme consists of three steps. First, for the BFSAR data after range compression and RCMC, a division procedure is carried out in the azimuth direction. The subblocks with the highest signal-to-clutter ratio along the range direction are selected after the azimuth segmentation procedure. Second, for the selected subblocks, the total NsRCM is estimated based on the minimum-entropy criterion. Based on the estimation results, different parts of the NsRCM are obtained by solving an ordinary differential equation. Third, a two-step compensation of the NsRCM is executed to reach the spatially variant correction. Simulations and experimental results are provided to demonstrate that our proposed scheme is effective for BFSAR imaging.

This article presents an adaptive large-size moving-target imaging technique for bistatic forward-looking SAR (BFL-SAR). The main problems of this issue are that not only the echo characteristics (including range cell migration and Doppler parameters) are unknown, but also the spatial-variances of these characteristics are nonlinear for different point-scatterers. The proposed technique relies on a proper processing of the data aiming at, first, to correct the range walk by applying keystone transform over the whole received echo, and then, the relationships between the unknown high-order RCM, the nonlinear spatial-variances of the Doppler parameters, and the speed of the mover, are established. After that, using an adaptive NLCS technique, not only the unknown high-order RCM can be accurately corrected, but also the nonlinear spatial-variances of the Doppler parameters can be balanced. Numerical simulations show the effectiveness of this adaptive large-size moving-target imaging technique to be employed in BFL-SAR frameworks.

Synthetic aperture radar (SAR) has been widely used in remote sensing. Feature extraction is a crucial step in SAR automatic target recognition (ATR). In this paper, Kernel Marginal Sample Discriminant Embedding (KMSDE) is proposed, which is based on kernel trick and manifold learning theory. In feature extraction via KMSDE, the original dataset is mapped to high dimensional space and manifold learning theory is introduced for dimensional reduction. KMSDE preserves local and class information of the original dataset, as well as gathers the with-class samples and separates between-class samples in the low-dimensional space. In addition, it employs information related to each sample's location in the dataset, which enhances the discriminative capability of the method. Compared to other SAR imagery feature extraction methods, the experiments based on MSTAR database show that the proposed method improves the recognition performance.

Recognizing the category attributes from the real world targets is one of the most challenging and attractive fields in synthetic aperture radar (SAR) application. It is an important issue to explore the spatial distribution characteristics of multi-aspect angle imagery in synthetic aperture radar automatic target recognition (SAR ATR). In this paper, we will research the spatial structure of multi-aspect angle SAR imagery through a visualization approach with real SAR data. Based on nonlinear dimensionality reduction, the representation of SAR samples is revealed in the low-dimensional Euclidean space, and the the nonlinear manifold distribution of multi-aspect angle SAR imagery is discovered. Besides, the regularity of that spatial distribution is summarized, i.e. the intrinsic structure of SAR images is parameterized by the aspect angles. The results of our research can provide a theoretical basis for SAR image classification and recognition algorithm designing.

Breaking through the forward-looking limitation of monostatic synthetic aperture radar (SAR), bistatic forward-looking SAR (BFL-SAR) brings much potential in areas such as automatic navigation and landing in bad weather conditions. For many cases of BFL-SAR mounted on maneuvering-platform (MBFL-SAR), neither the flight path is straight nor the topography is flat. Conventional imaging algorithms designed for linear trajectory and flat topography fail to handle this situation. In this paper, a full-aperture efficient extended nonlinear chirp scaling algorithm with topography compensation is proposed for the data processing of MBFL-SAR. Simulation is presented to verify the validity of the proposed algorithm.

Target recognition based on forward-looking imaging has many potential applications. However, the conventional algorithms fail to locate targets accurately due to the low resolution of forward-looking radar images. Meanwhile, the conventional algorithms always suffer from high computational complexity and cannot satisfy the requirement of real-time processing for high-speed platform. This paper proposes a novel target recognition method based on morphological and spatial features. The algorithm comprises of initial matching and dual verification algorithms based on image gray scale and a priori position information. It is demonstrated that the proposed algorithm can work well for the forward-looking radar images with coarse resolution and enjoy higher computational efficiency. Simulation and real data processing validates the superior performance of the proposed algorithm.

In bistatic forward-looking synthetic aperture radar (BFSAR), conventional autofocus algorithms cannot estimate the phase errors accurately when the range walk is compensated in the azimuthal time domain. This problem stems from the influence of the azimuth-variant Doppler coefficients after linear range cell migration correction in azimuthal time domain. In principle, nonlinear chirp scaling processing can be carried out to remove the azimuth variance. Nevertheless, Doppler azimuth variations of the Doppler parameters induced by the motion errors are not taken into consideration and serious defocus would emerge in the final image. To cope with such a problem, an estimation-evaluation-equalization scheme is proposed before conventional autofocus for BFSAR. Different from the conventional autofocus method, the azimuth-variant Doppler coefficients are additionally estimated and equalized before autofocus, and as a consequence, phase errors can be precisely estimated. The BFSAR data processing results demonstrate the validity of the proposed method on the improvement of autofocus in BFSAR.

In this paper, SAR MT imaging and velocity estimation method is proposed. The validity of this method is verified by numerical simulations. The main idea behind this method is to transform the PE problem to be a SOP problem. The advantages of this method include two main aspects: (i) This method can handle the MT imaging problem for different SAR modes, such as mono-static SAR, bistatic SAR, etc.; (ii) Both the along-track and cross-track velocities of theMT can be simultaneously estimated. In addition, since the focusing processing is conducted in 2D spectrum domain and the optimal criterion is the local minimum entropy, this method don't need to find a dominated point scatterer during the process.

Bistatic forward-looking synthetic aperture radar (BFL-SAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of an aircraft. Until now, BFL-SAR imaging theories and methods have been researched for stationary targets. Unlike the stationary target, the motion of a ground-moving target (GMT) induces unknown range cell migration and additional modulation of the azimuth signal. Thus, to finely image the GMT, one must obtain its velocity parameters accurately, but they are usually unknown. In this paper, a novel GMT imaging and velocity estimation method, which is based on mismatched compression, is proposed for BFL-SAR without a priori knowledge of the GMT's velocity parameters. The main idea behind mismatched compression is to use a presumed azimuth reference function for performing correlated operation with the azimuth signal of the GMT. In general, the Doppler parameters of the presumed azimuth reference function are different from those of the GMT's azimuth signal because the velocity parameters of the GMT are unknown. Therefore, the correlation operation referred to earlier is actually mismatched compression, and the resulting image is shifted and defocused. The shifted and defocused image is utilized to get the real Doppler and velocity parameters of the GMT. The advantage of this method is that not only the GMT can be well focused but also the GMT's velocity can be simultaneously obtained. In addition, this method needs only monochannel antenna. The proposed BFL-SAR GMT imaging and velocity estimation method is validated by numerical simulations.

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Owing to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In BFSAR, the compensation of the spatially variant motion errors is of great significance to get a well-focused image. In this paper, first, the spatial-variance properties of motion errors are analyzed analytically and quantitatively. Different from the side-looking monostatic and bistatic SAR, 2-D space-variant motion errors should be taken into consideration in BFSAR. The 2-D spatial variance of the motion errors can be categorized into two parts, range-variant motion errors of the transmitter and azimuth-variant motion errors of the receiver. Moreover, these two parts are independent of each other. Based on this property analysis, second, a motion compensation (MoCo) approach with cubic-order processing is proposed to deal with the spatially variant motion errors in BFSAR. In the cubic-order processing, the first-order MoCo is performed to correct the spatially independent motion errors on the raw data. The second-order MoCo is accomplished on the non-range-cell-migration (RCM) data to deal with the range-variant errors. After the second-order MoCo, since the signal direction of the non-RCM data coincides with the variant direction of the uncompensated phase errors, the azimuth-variant motion errors and slow time signal are coupled together. To cope with such a problem, the slow time signal is transformed into the direction perpendicular to the azimuth by a novel procedure named azimuth-slow time decoupling. At this stage, the coupling between the azimuth-variant motion errors and slow time signal has been eliminated. Azimuth-variant motion errors can be corrected precisely. Simulation and experimental results verify the effectiveness of the proposed method.

Geosynchronous synthetic aperture radar (GEO-SAR) offers new opportunities for continuous Earth observation missions with large coverage and short revisit cycle. The unique features of GEO-SAR present huge potentials for bistatic observation applications. In this paper, the concept and advantages of GEO bistatic SAR (GEO-BiSAR) are first investigated. The system consists of a GEO illuminator and an airborne receiver, such as an airplane or a near-space vehicle. Compared with a monostatic GEO-SAR system, the bistatic configuration can provide finer spatial resolution and higher signal-to-noise ration (SNR) with less system complexity. The spatial resolution characteristics are then analyzed based on generalized ambiguity function, where the time-varying GEO velocity, Earth rotation, and ellipsoid Earth surface are taken into consideration. Meanwhile, the bistatic SNR is analyzed using the integration equation model. In this paper, the mission design for GEO-BiSAR aims at identifying a set of receiver flight parameters and bistatic configurations to obtain the desired spatial resolution and SNR. Based on the desired imaging performance of a specific application background, the mission design process is modeled as a nonlinear equation system (NES). Finally, a mission design method based on fast nondominated sorting genetic algorithm is proposed to solve the NES and obtain multiple optimal solutions to guide the receiver flight missions. Examples of the mission design process are given to validate the effectiveness of the proposed method. The results of the mission design can be conveniently used to guide the receiver flight mission for the desired imaging performance, which is highly desirable in practical applications.

With the geosynchronous synthetic aperture radar (SAR) satellite as the transmitter, the unmanned aerial vehicle (UAV) can passively receive the echo within the illuminated ground area and achieve 2-D imaging of the interested target. This SAR system, known as GEO-UAV bistatic SAR, is capable of autonomously accomplishing the bistatic SAR mission in rough terrain environments by prespecifying a path for the UAV receiver. In this paper, the GEO-UAV bistatic SAR system is first investigated. The practical advantages and spatial resolution are then analyzed in detail. The spatial resolution of GEO-UAV bistatic SAR is dependent on the observation geometry, which is determined by the UAV path. Therefore, the path planning for GEO-UAV bistatic SAR aims at identifying a set of optimal paths for the UAV receiver to travel through a 3-D terrain environment that simultaneously guarantees the safety of the UAV and achieves SAR imaging with optimized performance during the flight. The path planning is modeled as a constrained multiobjective optimization problem (MOP), which accurately represents the two main aspects for the path planning problem, i.e., UAV navigation and bistatic SAR imaging. Then, a path planning method based on a constrained-adaptive-multiobjective-differential-evolution algorithm is proposed to solve the MOP and generate multiple feasible paths for the UAV receiver with different tradeoffs between navigation for UAV and bistatic SAR imaging performance. The GEO-UAV bistatic SAR mission designer can choose a path from the solution set according to the application requirements, which makes the method more pragmatic.

The ground moving target (GMT) model has been widely employed in modern coherent radar systems, such as the synthetic aperture radar (SAR) and the bistatic SAR (BiSAR). For the coherent radar systems, GMT imaging necessitates the compensation of the additional azimuth modulation without a priori knowledge of the GMT's motion parameters. That is to say, it is necessary to estimate the Doppler parameters of the GMT before the azimuth compression processing. For conventional estimation methods, such as the map drift (MD) method and the phase gradient auto-focus (PGA) method, a searching procedure is necessary and leads to an expensive computational cost. In this paper, a Doppler parameter estimation method based on mismatched compression is proposed. One advantage of this method is that it doesn't need the searching procedure. In addition, another advantage of this method is that both the Doppler centroid and the Doppler frequency rate of the GMT can be simultaneously estimated according to the relationships among the Doppler parameters, the positional offset and the boarding width of the mismatched imaging result. The theoretical analysis and numerical simulations validate that the proposed method works well with different signal to noise ratio.

This paper presents a deconvolution algorithm based on the Maximum likelihood (ML) criterion to realize azimuth angular superresolution of sea-surface target in the background of sea clutter. Firstly, the received signal of real-beam image in azimuth dimension was modeled as the convolution of antenna pattern and target scattering. Then the ML objective function was built according to the assumption that the sea clutter obeys Rayleigh distribution. Finally, the iterative expression was derived to recover the original scattering of sea-surface target. Compared to Poisson-based ML deconvolution method, the assumption of clutter distribution is more reasonable for the practical background. Simulations are given to verify the effectiveness of the algorithm.

Highly squint synthetic aperture radar (SAR) data focusing is a more challenging and difficult task than the side-looking SAR due to the strong 2-D coupling of echo signal induced by the imaging mode. Although several algorithms have been proposed, they fail to take into consideration the spatial variance of linear range cell migration (RCM). Moreover, the RCM correction (RCMC) process in range frequency and azimuth time domain by phase multiplication brings the problem of azimuth variation of the Doppler centroid, which has a great influence on the azimuth focusing. In this letter, an algorithm based on keystone transform (KT) and azimuth extended nonlinear chirp scaling (ENLCS) is derived to deal with these problems. A new method for higher order RCMC is derived to remove the residual RCM after KT. Then, azimuth ENLCS is performed to equalize the azimuth-variant Doppler centroid and FM rate. The simulation results verify the effectiveness of this algorithm.

This paper presents a near-field 3-D synthetic aperture radar (SAR) imaging system for which the 2-D aperture is radially scanned. Compared to the current SAR imaging systems, the proposed system has several advantages such as quick data collection, full 360° inspection of target, and simple image formation processing. However, in radial scan, the samples do not fall on a Cartesian grid, which prevents us from using the fast Fourier transform (FFT) to form SAR image without calling for interpolation. In this paper, the 2-D nonuniform FFT (NUFFT) is used for dealing with the problem. After 2-D NUFFT of the radial sampled data, the 3-D reflectivity image can be efficiently reconstructed by using the 3-D version of the range migration algorithm. The Stolt mapping has been implemented implicitly by another 1-D NUFFT to reduce the artifacts caused by the conventional interpolation processing. In addition, to alleviate the data sampling burden, a compressed 2-D slow-time sampling strategy is also discussed. Finally, the proposed Rad-SAR system and the imaging method are demonstrated using near-field wideband simulation data.

The combination of frequency-modulated continuous-wave (FMCW) technology and bistatic synthetic aperture radar (SAR) promises a small, light-weight, cost-effective and high-resolution remote sensing system. In this paper, an Omega-k imaging algorithm based on linearization theory to focus the raw data of translational invariant (TI) bistatic FM-CW SAR is proposed. This method utilizes a bistatic point target reference spectrum (BPTRS) of generalized Loffeld's bistatic formula (GLBF), where the motion during the long signal duration time is taken into account. The proposed BPTRS significantly simplify the TI bistatic FMCW SAR processing. Based on the spatial linearization of GLBF, the Stolt transformation is further derived. Finally, simulation experiments are carried out to verify the effectiveness of the proposed method.

Current literatures and reports about Bistatic forward-looking SAR (BFSAR) imaging theories are mostly concentrated on static scene. In this paper, a novel GMT detection and imaging method in Doppler-rate domain for BFSAR is presented. The steps of this method involve bulk-deramp filtering operation, Keystone transform (KT), extend azimuth nonlinear chirp scaling (EA-NLCS) processing and the last detection step based on product second-order ambiguity function (PSAF). After the above steps of the method, if the PSAF of a certain range bin has more than one sharp peak, we can argue that there is a GMT in the relative range bin. Then using the GMT's Doppler rate estimated by PSAF, the echo of GMT can be focused, thus the goals of GMT detection and imaging for BFSAR are achieved. Numerical simulations verify the effectiveness of the proposed method.

One-Stationary bistatic forward-looking SAR (OSBFSAR) is a SAR system that using a geostationary satellite or a near-space low-speed platform as the transmitter and using an airborne or a missile as the receiver. Current literatures and reports about OSBFSAR are mostly about imaging theory and stationary scene imaging methods. In this paper, we propose an OSBFSAR moving target detection and imaging method with a linear antenna array. This method is associated with the two-dimensional spatial variation imaging technology of OSBFSAR and the extended velocity-SAR technology. Using this method, not only the stationary clutter can be suppressed but also the moving targets can be detected and focused well. Some numerical experiments are given to verify the validity of the method shown in this paper.

Ground moving target detection (GMTD) for squint synthetic aperture radar (SAR) is a more challenging task than that of the broadside SAR. The severe range and azimuth coupling induced by the squint mode combined with the unknown motion parameters makes the problem of indicating the moving targets more involved. In this paper, a novel ground moving target detection method for squint SAR is proposed. Firstly, an extended Keystone Transform (KT) is performed to remove the range cell migrations (RCMs) of both the stationary and moving targets. Secondly, the range compressed data undergoes two extended azimuth nonlinear chirp scaling (EANLCS) process separately with different sets of parameters based on the stationary scene. Then, after azimuth de-ramp processing, two images are obtained where all the targets are well focused except for the moving targets. Finally, by subtracting the amplitudes of the two images, the stationary targets are eliminated and the moving targets are detected. This algorithm is suitable for large squint angle cases and is computationally efficient. Simulation results verify the effectiveness of the algorithm.

The butterfly algorithm is introduced to achieve fast imaging of near space circular synthetic aperture radar (SAR) raw data with high resolution and large scene. The fast algorithm benefits from projecting the frequency domain signal after matched filtering to subimage. Quad tree structures constructed on both signal and image spaces are used to project signal recursively. Following the traversal of the quadtree, the image resolution is ameliorated gradually. The algorithm run on parallel processors and fast imaging is accomplished. Except for the ability of precise and quick imaging, large scene imaging is also achieved. The simulation results show the high quality image with good resolution.

Angular super-resolution performance is the key problem in the field of radar imaging. In this paper, we propose an approach to radar angular super-resolution through deconvolution, which is able to increase the resolution of radar image beyond the limitation of system parameters. It relies on the Bayesian formulation approach that enables to incorporate the prior information about the system and the statistical characteristics of scene. We first formulate the radar angular superresolution problem as an linear inverse problem and then convert it to a maximum a posterior (MAP) task using Bayesian theory. We then solve the MAP problem in a convex optimization framework using a shrinkage based iterative procedure, leading to algorithm that guarantees the solution to converge the global maximizer of an associated MAP criterion. Numerical experiments with synthetic data demonstrate the performance of proposed angular super-resolution algorithm.

This paper present a novel superresolution algorithm for real-beam scanning radar based on the iterative adaptive strategy. Firstly, we establish the objective function based on the minimum mean-square error (MMSE) criterion, then we build the iterative expression by update the covariance matrix. This algorithm has better superresolution performance than traditional deconvolution method. Simulation results verified the analysis before.

This paper present a superresolution algorithm for forward-looking imaging of scanning radar based on weighted least squares method. This algorithm utilized the weighted vectors to structure the objective function, and introduced the diagonal loading technique to obtain more robust superresolution result. Simulation results verified the performance of our algorithm.

Near-space Synthetic Aperture Radar (SAR) possesses useful features such as High-Resolution Wide-Swath (HRWS) and high revisiting frequency etc., which are difficult for the conventional SAR, e.g. spaceborne and airborne SAR, to simultaneously provide. Slow-speed SAR is introduced to solve the contradiction between high resolution and wide swath of Near-space SAR and a frequency domain algorithm based on Two-Step Processing Approach (TSPA) and Stolt interpolation is proposed. Simulations are provided to demonstrate the validity of the proposed algorithm.

This paper presents a two-dimensional (2-D) generalized omega-K algorithm for high-precision processing of the data of missile-borne Synthetic Aperture Radar (SAR) with constant acceleration. The almost adequate expression of the 2-D spectrum of the echo signal is obtained by the Method of Series Reversion (MSR). Then a 2-D generalized Stolt interpolation is carried out. The proposed algorithm can be applied to situations involving large dive and squint angle and high maneuvering speed. Simulations are presented to demonstrate the validity of the proposed algorithm.

Doppler parameters, range cell migrations (RCMs), and higher order coupling terms in the raw data of translational variant bistatic synthetic aperture radar (TV-BiSAR) exhibit 2-D spatial variations. The 2-D spatial variations result in significant performance degradation of TV-BiSAR imaging. To solve these problems, an Omega- k imaging algorithm based on linearization theory is proposed in this letter. Compared with the traditional Omega- k algorithm that uses the 1-D Stolt transformation to eliminate only the spatial variations in the range direction, the proposed algorithm applies the 2-D Stolt transformations to achieve the goal of both 2-D frequency linearization and 2-D spatial-domain linearization. After the 2-D Stolt transformations, a focused image can be obtained by performing a 2-D inverse fast Fourier transform (IFFT). In the proposed Omega- k imaging algorithm, the 2-D spatial variations of the Doppler parameters, RCM, and higher order coupling terms for TV-BiSAR can be simultaneously eliminated. However, in previous publications about BiSAR imaging algorithms, the spatial variations in the azimuth direction are barely considered, which reduces the imaging accuracy. Numerical simulations verify the effectiveness of the proposed method.

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications, such as self-navigation and self-landing. In the mode of BFSAR with a stationary transmitter (ST-BFSAR), the two-dimensional spatial variation makes it difficult to use traditional data focusing algorithms. In this letter, an imaging algorithm based on keystone transform and nonlinear chirp scaling (NLCS) is proposed to deal with this problem. Keystone transform is used to remove the spatial variation of range cell migration. NLCS can eliminate the variation of azimuth reference function. Numerical simulations show that by combining first-order keystone transform and azimuth NLCS operation, the raw data of ST-BFSAR can be well imaged.

In translationally variant (TV) bistatic synthetic aperture radar (BSAR), 2-D spatial variation is a major problem to be tackled. In this paper, a generalized Omega-K imaging algorithm to deal with this problem is proposed. The method utilizes a point target reference spectrum of the generalized Loffeld's bistatic formula (LBF) (GLBF). Without the bistatic-deformation term, GLBF is the latest development of LBF. Similar to the monostatic case, it has a much simpler form than other point target reference spectra. Based on the spatial linearization of GLBF, the Stolt mapping relationship is derived. Different from the traditional Omega-K algorithms for monostatic SAR and translationally invariant BSAR, this approach uses a 2-D Stolt frequency transformation. Through this transformation, the method can deal with the 2-D spatial variation. It can also consider the linear spatial variation of Doppler parameters, which is usually not considered in the previous publications on bistatic Omega-K algorithms. This method can handle the cases of TV-BSAR with different trajectories, different velocities, high squint angles, and large bistatic angles. In addition, a compensation method for the phase error caused by the linearization is discussed. Numerical simulations and experimental data processing verify the effectiveness of the proposed method.

In this paper, an omega-K imaging algorithm to focus the raw data of translational invariant (TI) bistatic synthetic aperture radar (BSAR) is proposed. The method utilizes a point target reference spectrum of generalized Loffeld's bistatic formula (GLBF). Without the bistatic deformation term, GLBF is the latest development of Loffeld's bistatic formula. It is comparable in precision with the method of series reversion (MSR), but it has a much simpler form than MSR and a similar form to a monostatic case. Based on the spatial linearization of GLBF, the Stolt transformation relationship is derived. The method can consider the linear spatial variation of Doppler parameters, which is always ignored in previous publications about bistatic omega-K algorithms. This method can handle the cases of TI BSAR with high squint angles and large bistatic degrees. In addition, a compensation method for the phase error caused by the linearization is discussed. Numerical simulations and experimental data processing verify the effectiveness of the proposed method.

Angular resolution of real aperture radar is limited by the aperture size and wavelength. In this paper, a novel angular superresolution algorithm based on Fourier-Wavelet regularized deconvolution (ForWaRD) is proposed. Orthogonal expansion and scalar shrinkage in a tandem transform domain lie at the core of the ForWaRD. Duo to deconvolution is a noise sensitive process, the method is used to deconvolve and denoise the echo signal simultaneously. We also conclude that optimum performance of this algorithm is simultaneously determined by the Fourier structure of the antenna pattern and the wavelet structure of the surface scatterers. Simulation results show that the algorithm can efficiently enhance the resolution of scanning radar under a low SNR environment.

Near space slow speed SAR owns the ability of sustainable and large-scene imaging. However, the output speed of azimuth image is slow due to the slow speed motion, which is a disadvantage for some applications. In this paper, the terrain observation by progressive scans (TOPS) mode is applied in near space slow speed SAR, and the signal properties are analyzed.

Due to left/right Doppler ambiguity, and the small difference in Doppler frequencies of adjacent points in flight direction, conventional monostatic SAR can not be used for forward-looking imaging. Then bistatic SAR with transmitter and receiver mounted on different platforms is coming into the eyes of researchers. In this paper, the feasibility of forward-looking imaging using airborne bistatic SAR is studied. Firstly, the potential two-dimension resolution ability of bistatic SAR for forward-looking imaging is discussed from the aspect of iso-Doppler and iso-range line. Then the airborne bistatic SAR experiment using a side-looking airborne transmitter and a forward-looking airborne receiver is described, and the preliminary results are presented. To the authors' knowledge, this is the first bistatic forward-looking SAR experiment in the world that both transmitter and receiver are mounted on aircraft platforms.

The generation of SAR echo is very important for both the system design and the validity test of imaging algorithm. Common time-domain-based echo generation methods are usually time-consuming and inefficient. Hence, this article will focus on the research of efficient echo generation methods. In translational variant bistatic mode, two-dimensional (2D) spatial variability is the major problem to be faced with in efficient echo generation. To solve this problem, this paper proposes an efficient echo generation method of translational variant bistatic SAR based on 2D inverse Stolt mapping. The proposed method first uses 2D FFT to generate 2D linear spectrum, and then completes nonlinearized operation of the spectrum by 2D inverse Stolt mapping. This process fully considers the 2D spatial variability in translational variant bistatic mode, which guarantees the accuracy of the generated echo. Finally, simulation results are presented to verify the validity of the proposed method.

One-stationary bistatic side-looking synthetic aperture radar (OS-BiSAR) data are more challenging to process than the translational azimuth-invariant bistatic counterparts because 2-D spatial variances exist in OS-BiSAR. To overcome the problem, extended-keystone-transform (EKT)-nonlinear-chirp-scaling (NLCS) imaging algorithm is proposed in this letter. The key steps of the proposed algorithm are deducing the twice EKTs to deal with the 2-D spatial variance of range cell migrations and equalizing the 2-D spatial variant Doppler FM rates by using the associative NLCS. As a result, raw data of OS-BiSAR can be well focused even with the 2-D spatial variances. The simulations given at the end of this letter verify the effectiveness of this imaging algorithm.

Non-uniform FFT (NUFFT) algorithm is widely applied to Communication, Medical Imaging, Radio Astronomy and so on. NUFFT contributes to SAR imaging and Near-Space SAR also becomes popular recently years. It is difficult to get accurate SAR image in Near-Space because of the obvious motion error and Wide-Swath when the vehicle is slow, so we propose an approach which uses twice NUFFT in ω - k algorithm to solve the problem in this paper. The first NUFFT can solve non-uniform sampling along the track, the second NUFFT can replace the Stolt interpolation and the final range inverse FFT in ω - k algorithm to increase the computational efficiency without reducing the accuracy of the SAR image. The new method is especially suited for Near-Space SAR imaging when the speed of the vehicle is slow.

For high-quality synthetic aperture radar (SAR) processing, Doppler centroid estimation is an essential procedure. An incorrect Doppler centroid would cause a loss of signal-to-noise ratio, an increase in the azimuth ambiguity level, and a shift in the location of the target. Based on the analysis of the range migration characteristic of the transmitter-fixed bistatic forward-looking SAR, a geometry-based Doppler centroid estimator is proposed in this letter. By searching for the range walk slope based on the minimum entropy, this method estimates the unambiguous Doppler centroid directly. Simulations validate the effectiveness of this method.

Focusing bistatic SAR data in frequency domain requires the two dimensional (2D) point target reference spectrum (PTRS). In this paper, a 2D PTRS is derived based on Lof feld's bistatic formula (LBF). The spectrum in this paper combines the influence of the Doppler centroids and frequency modulated rates of the transmitter and receiver on the total Doppler contributions. The essential of this paper is to model the Doppler contributions of the transmitter and the receiver using power series. It can be used in extreme bistatic (like hybrid spaceborne/airborne) and high squint (like bistatic forward-looking) cases. The accuracy of the PTRS is verified using numerical simulation of point target.

Doppler centroid is an important parameter for high quality synthetic aperture radar (SAR) imaging. In this paper, several geometry-based Doppler ambiguity resolvers are discussed and compared. The result show that these methods can give accurate estimation of Doppler ambiguity number, and the speed of the iterative scheme and improved Radon transform scheme is much faster than conventional Radon transform method.

Doppler rate is an important parameter in synthetic aperture radar (SAR) imaging. Incorrect Doppler rate would cause great degradation of image quality. In this paper, by estimating the Doppler rate in high-contrast scene first, and then utilizing the fact that Doppler rate is inversely proportional to range, the Doppler rate in low-contrast scene is estimated indirectly and effectively.

With appropriate geometry configurations, bistatic Synthetic Aperture Radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In this paper, we present a vehicle-borne BFSAR experiment, including the imaging principle, system setup, echo property and data processing result. In the experiment, the transmitter is located stationarily besides the moving receiver. The transmit and receive antennas both point to the up forward-looking area to illuminate a point target. To the authors' knowledge, this is the first result of BFSAR with stationary transmitter in the world right now.

For high-quality synthetic aperture radar (SAR) processing, Doppler centroid estimation is an essential procedure. An incorrect Doppler centroid would cause a loss of SNR, an increase in the azimuth ambiguity level, a shift in the location of the target, etc. An improved Radon-transform-based Doppler centroid estimation scheme of bistatic forward-looking SAR is proposed in this letter. First, this scheme performs edge detection on the range-compressed data and then does the coarse and fine Radon transforms to estimate the Doppler centroid. Simulations and real-data experiments validate the effectiveness of this method.

Bistatic synthetic aperture radar (BSAR) with one fixed station (OF-BSAR) can be used in wide area surveillance, interferometry and etc. This paper analyzed the spatial spectrum of OF-BSAR. Analytical expressions of the spatial spectrum was given. Using this result, we can determine the resolution performance of OF-BSAR.

With appropriate geometry configurations, bistatic Synthetic Aperture Radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. With such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In this paper, based on the resolution calculation using gradient theory, we give a general rule to determine the optimal geometry configuration of different modes of BFSAR. The results can be used to design BFSAR flight campaign and measure the performance of a specific BFSAR system.

This letter concerns the resolution-fusion method for linear array 3-D imaging SAR (LASAR). Limited by the length of the linear array, the cross-track resolution of LASAR is often lower than that in the along-track direction. To overcome this disadvantage, we assume that there are two LASAR systems whose trajectories are orthogonal to each other. Thus, we obtain a row low-resolution image and a column low-resolution image of the same scene. Using the orthogonal complement decomposition technique, we fuse the two images into one quasi-high-resolution image. Moreover, we find that the fusion distortion is unavoidable and contains the high-frequency component in both row and column directions. The information loss ratio is (N- 1)2/N2(Ndenotes the ratio of low resolution to high resolution). For a smooth image, the energy loss ratio is near to zero. With the increase of the noise energy, the energy loss ratio increases correspondingly. When the noise submerges the image completely, the energy loss ratio converges to the information loss ratio.

In this paper, the signal properties for the translational invariant case of bistatic synthetic aperture radar (SAR) based on the squint mode are deduced. Bistatic range history, point target response in time and frequency domains, Doppler properties and resolutions are presented in terms of the platform coordinates and the squint angles. The results are valid for large aperture and large squint angles. The accuracy of quadratic approximation in bistatic SAR is also discussed.