This paper investigates the robust wideband channel estimation problem in the millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. In such a scenario, the beam squint effect that the array response vectors vary with different frequencies and the impulsive noise can occur, which pose great challenges for accurate channel estimation. Directly applying the existing channel estimation methods usually suffers significant performance degradation, since they are proposed based on the assumptions of frequency-invariant array response vectors and Gaussian distributed noise. To address these issues, this paper proposes a novel wideband channel estimation method with robustness to impulsive noise. Specifically, the proposed method incorporates a cyclic refinement step to overcome the estimation inaccuracy caused by the greedy nature of matching-pursuit-esque algorithms. In particular, the generalized$\ell_{p}$-norm minimization criterion is adopted in Newton's method to improve the performance robustness against the non-Gaussian impulsive noise. Numerical results are provided to verify the superior performance of the proposed method over the existing representative benchmarks.

The target detection task of ground penetrating radar (GPR) based on deep learning has received widespread attention. Previous studies focused more on the features of targets in images and achieved excellent performance. However, in the practical application of these methods, GPR image is cut into several slices for feeding to the model for training and inference, which not only requires accumulating pulses but disrupts the continuity of pulse information, making it difficult to communicate semantic information of different parts of the same pulse in different slices. To address these issues, this letter proposes a pulse-by-pulse target detection model, namely, 1-D mix attention (MA) TransUnet, for GPR, avoiding pulse accumulation and preserving the continuity of pulse information. In structure, the spatial and channel mixed attention mechanism replaces skip connections in 1-D Unet, which effectively enhances the target features in pulse data. In addition, transformer block (TB) based on multihead self-attention (MSA) is applied to the downsampling feature map of 1-D Unet, which allows the model to effectively understand the global semantic information and suppress nontarget features that are similar to the target features in pulse data. Finally, the effectiveness of 1-D MA TransUnet is validated using GPR pulse data containing steel mesh as a case study. The model achieved an accuracy of 83.07%, a recall rate of 71.64%, and an F1-score of 76.93%, respectively.

Structure-from-motion (SfM) is the concept of estimating both sensor pose and three-dimensional (3D) scene structure from input images. The difficulty with synthetic aperture radar (SAR) SfM stems from the non-linearity of radar imaging, making it hard to decouple and calculate radar pose and structure. Existing methods typically address this issue by simplifying the imaging process or introducing auxiliary data. In this paper, we propose to jointly solve radar pose and structure by formulating an optimization problem, which only needs two-dimensional (2D) SAR observations as input. The objective function is written as the summation of all reprojection errors established by the precise range-Doppler (RD) imaging model, with radar poses embedded into the transformations between the sensor and scene coordinate systems. The rotational components in radar poses are represented as matrices and constrained on the special orthogonal groupSO(3). A special orthogonal Riemannian conjugate gradient algorithm (SO-RCG) is then proposed to solve the optimization problem. The proposed algorithm preserves the orthogonality of rotation matrices and updates all variables iteratively. Furthermore, we deduce that only five degrees of freedom (DoFs) in radar pose are involved in determining the imaging results of targets. We also discuss the ambiguity issue in multiview SAR observation leading to local minimums and introduce strategies against ambiguity. Experimental results on real-measured and simulated datasets show that the proposed algorithm is effective on both near- and far-field cases, and can be applied to both side-looking and squint SAR.

Despite notable advancements in remote sensing vision-language models (VLMs), existing models often struggle with spatial understanding, limiting their effectiveness in real-world applications. To push the boundaries of VLMs in remote sensing, we specifically address vehicle imagery captured by drones and introduce a spatially-aware dataset AirSpatial, which comprises over 206K instructions and introduces two novel tasks: Spatial Grounding and Spatial Question Answering. It is also the first remote sensing grounding dataset to provide 3DBB. To effectively leverage existing image understanding of VLMs to spatial domains, we adopt a two-stage training strategy comprising Image Understanding Pre-training and Spatial Understanding Fine-tuning. Utilizing this trained spatially-aware VLM, we develop an aerial agent, AirSpatialBot, which is capable of fine-grained vehicle attribute recognition and retrieval. By dynamically integrating task planning, image understanding, spatial understanding, and task execution capabilities, AirSpatialBot adapts to diverse query requirements. Experimental results validate the effectiveness of our approach, revealing the spatial limitations of existing VLMs while providing valuable insights. The model, code, and datasets will be released at https://github.com/VisionXLab/AirSpatialBot.

Synthetic aperture radar (SAR) images are inherently affected by speckle noise. Deep learning-based methods have shown good potential in image denoising task. Most deep learning methods for denoising focus on additive Gaussian noise removal. However, SAR images are usually contaminated by non-Gaussian multiplicative speckle noise. In this paper, we propose a novel deep unrolling network named SAR-DURNet to deal with the SAR image despeckling problem. We establish optimization problem of speckle noise removal by using the priori of noise distribution, which can be sovled by half-quadratic splitting (HQS) method with iterative steps. We unroll the iterative process into a trainable deep unrolling network(SAR-DURNet). The parameters of the SAR-DURNet are trained end-to-end with simulated SAR image dataset. Experimental results on simulated test data and real SAR data show that the proposed approach has superior results in terms of quantitative performance metrics and the preservation of intricate visual details, compared to several well-known SAR image despeckling methods.

Tensor completion has attracted increasing attention in signal processing, computer vision, and biomedical engineering. By using nuclear norm minimization, a tensor completion problem can be converted into a convex program and enjoys properties gained from matrix completion. The low rank property has been widely used for tensor/matrix completion. However, the prior subspace information can also be utilized, which has been ignored and does not exhibit its full power in the existing formulation. In this paper, we propose a new framework leveraging tensor subspace prior for the sum of nuclear norm (SNN) minimization, which supports a range of tensor decompositions. By using the knowledge of the self-prior (SP)/nonself-prior (NSP) and further designing an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM), the performance of tensor completion can be enhanced. The superiority of the proposed method is verified by extensive numerical experiments.

Due to existence of the long along-track baseline (ATB) among the different satellites in a distributed space-based radar (DSBR) system, a large number of grating lobes appear in radar returns, causing the non-continuous detection phenomenon of an air moving target (AMT). To solve this problem, in this paper, a novel approach for ATB distribution design in a DSBR system is proposed. In the proposed algorithm, to reduce the amount of spatial ambiguity points located at the main-lobe region and achieve the best air moving target indication (AMTI), the maximum target detectability ratio (TDR) in the main-lobe region is chosen to be the criteria for the optimal ATB design. The effectiveness of the proposed method is verified by the simulated multi-channel radar data in a DSBR system.

GPR clutter removal significantly benefits subsequent target recognition, detection, and imaging, enhancing the subsequent processing quality. Traditional clutter removal approaches can only remove noise in simple environments. To solve this problem, We proposed a novel improved triplet attention u-net(ITU-Net) which focuses on the hyperbolic feature w e need while disregarding irrelevant ground clutter and other background noise. The ITU-Net network enhances image reconstruction capability and facilitates rapid image processing. The improved triplet attention module captures cross-domain interaction between any two domains between H, W, and C and considers long-distance dependencies separately in H, W, and C. The experimental results demonstrate that we can effectively retain the information of the hyperbolas while eliminating noise in complex environments.

This paper proposes a fast two-step method which aims to address the SAR pose estimation problem and enable Unmanned Aerial Vehicles (UAVs) the self-localization capability under harsh conditions. Firstly, the monocular SAR pose estimation is formulated as a Circular-n-Point (CnP) problem based on frequency-domain SAR imaging mechanism. Then, we propose to decouple the motion components of radar platform and solve the overdetermined equations of direction and location sequentially. Experimental results validate the effectiveness, accuracy, and efficiency of the proposed method.

Transformer has shown great capability in remote sensing and automatic target recognition (ATR). Due to the self-attention mechanism, the Transformer could extract global features while parallelizing training. However, the computational costs and power consumption are challenging the electronic computing techniques. Here, we develop a Transformer-based optronic neural network (TOPNN) for synthetic aperture radar (SAR) target recognition. We implement the self-attention mechanism in optics, significantly reducing the network computational costs. Compared with digital techniques, the TOPNN promises the speed of light, low computational costs, and low power consumption. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the feasibility and efficiency of TOPNN for SAR target recognition.

Marine surveillance radar can monitor the maritime environment under all weather conditions, but the presence of sea clutter significantly impacts its target detection performance. To effectively mitigate the influence of sea clutter on radar imaging, this paper proposes a neural network based on DenseNet combined with wavelet transform. The wavelet transform, known for its reversibility that preserves the original image information, is capable of extracting features at different levels of detail. DenseNet enhances the flow of extracted features, effectively alleviating the issues of gradient explosion or vanishing. The network is evaluated using data collected by the IPIX radar as the sea clutter noise dataset. Under various input conditions with different clutter-to-signal ratios, the proposed network achieves an average improvement of 18.78dB in clutter-to-signal ratio. Experimental results demonstrate the network’s effectiveness in suppressing sea clutter noise.

Land cover (LC) classification via remote sensing is crucial for ecosystem monitoring and urban planning but faces the challenge of inconsistent multimodal data availability. Current techniques often falter with incomplete modalities, resulting in reduced performance and adaptability. Addressing these issues, this study propose the Transformer-based Incomplete Multi-Modal Learning (TIMML) framework. TIMML incorporates a Bernoulli indicator module during training to facilitate adaptation to missing modalities. This module, in tandem with a fusion token, is instrumental in enabling the model to handle the random omission of modalities by selectively nullifying data streams and effectively aggregates information from the remaining available modalities. Moreover, TIMML integrates a modality-aware regularization module designed to enhance the stability of the feature extraction process, especially when perturbed by the Bernoulli indicator during training. Our comprehensive experiments demonstrate that TIMML not only proficiently manages the challenge of missing modalities but also outperforms existing methods in LC classification tasks, marking a significant advancement in the field.

Change detection (CD) plays a crucial role in various physical applications. Recently, many studies have turned to semi-supervised semantic segmentation methods to alleviate the reliance on extensive annotations. However, these methods tend to neglect the presence of the cross-temporal background noise in bi-temporal remote sensing image (RSI) pairs, which can mislead model’s prediction. Therefore, this work introduces an innovative Multi-view Feature Enhancement (MFE) method for semi-supervised CD. To mitigate the influence of inherent cross-temporal background noise in RSI pairs, we design a multi-view strategy and temporal-perception data augmentation for feature enhancement. According to the experimental results on two publicly available datasets, our proposed method outperforms several state-of-the-art (SOTA) methods in terms of intersection over union (IoU) and overall accuracy (OA).

Synthetic aperture radar is a widely used technology. However, current 3D imaging algorithms either require a large number of passes or have limited elevation resolution due to carrier size. In this paper, a 3-D SAR imaging method based on 2-D wavefront modulation is proposed. The method achieves the same range and azimuth resolution as traditional SAR, while also obtaining high elevation resolution with limited carrier size and number of flights. The wavefront modulation function requires a smaller correlation coefficient between two elevation angles with a larger difference. Finally, a simulation is conducted to demonstrate the effectiveness of the proposed method.

For maneuvering targets with weak surface reflections, the conventional ISAR imaging algorithms based on the motion parameter estimation for each scatterer may have a poor performance under low signal-to-noise ratio (SNR) scenario. To tackle this problem, a novel ISAR imaging algorithm is proposed in this letter. The effects of translational motion, the high-order migration through resolution cells (MTRC), and the nonstationary phase distribution are simultaneously eliminated by constructing the compensation function and utilizing the time polynomial rescaling-nonuniform fast Fourier transform (TPRS-NUFFT). Then, the particle swarm optimization (PSO) algorithm is applied to accomplish the motion parameter estimation. Finally, a high-resolution ISAR image can be obtained after motion compensation. Both simulation and real-measured data processing results demonstrate that the proposed algorithm can obtain focused ISAR image and improve the SNR by 29.8 dB.

The utilization of unlabeled samples has contributed significantly to the achievements of semi-supervised methods in optical remote sensing image (ORSI) scene classification. However, existing methods face the challenge of effectively integrating labeled and unlabeled data during model training. To mitigate these challenges, a semi-supervised label and embedding consistency network (SS-LEC) is proposed for OSRI scene classification. Specifically, given an image, SS-LEC enables the high-confidence prediction from a weak-augmentation view consistent with the prediction from a strong-augmentation view, while also ensuring consistency in embeddings derived from middle-augmentation views. Moreover, a soft learning schedule is proposed to strategically focus on varied consistency tasks at different stages of training. Our experiments on two ORSI datasets showcase SS-LEC’s superior classification performance over existing semi-supervised methods. Notably, under label-scarce scenarios with only four labeled images per category, SS-LEC achieves classification accuracies of 92.04% on the EuroSAT dataset and 70.19% on the NWPU-RESISC45 dataset. These results set new benchmarks and demonstrating superior classification performance in challenging conditions with limited labeled data.

In recent years, enormous studies have been conducted to improve the land cover (LC) classification performance of multimodal remote sensing (RS) data, which outperforms single-modal-based methods by a large margin due to information diversity. To go a step further, we develop a two-branch patch-based convolutional neural network (CNN) with an encoder–decoder (ED) module to fuse multimodal RS data information. A knowledge distillation in model (DIM) module is proposed to guild per-modality encoder learning with the final fused information to enable multimodal data fusion more effectively. Moreover, utilizing multimodal information to guide single-modal learning still remains to be explored. To this end, a knowledge distillation cross-model (DCM) module is designed to improve single-modal LC classification with multimodal knowledge distillation, which bridges the gap between single-modal-based and multimodal-based methods. In particular, the multimodal-based method is taken as a teacher to transfer knowledge to single-modal-based methods. Extensive experiments are carried out on two multimodal RS datasets, including hyperspectral (HS) and light detection and ranging (LiDAR) data, i.e., the Houston2013 dataset, and HS and synthetic aperture radar (SAR) data, i.e., the Berlin dataset. The results demonstrate the effectiveness and superiority of the proposed multimodal fusion strategy in comparison with several state-of-the-art multimodal RS data classification methods. Also, the proposed DCM module improves the LC classification performance of single-modal methods by a large margin.

Patch-based adversarial attacks have increasingly aroused concerns due to their application potential in military and civilian fields. In aerial imagery, numerous targets exhibit inherent directionality, such as vehicles and ships, giving rise to the emergence of oriented object detection tasks; similarly, adversarial patches also exhibit intrinsic orientation due to their lack of perfect symmetry. Existing methods presuppose a static alignment between the adversarial patch’s orientation and the camera’s coordinate system – an assumption that is frequently violated in aerial images, whose effectiveness degrades in real-world scenarios. In this paper, we investigate the often-neglected aspect of patch orientation in adversarial attacks and its impact on camouflage effectiveness, particularly when the orientation is not congruent with the target. A new Directional Guided Attack (DGA) framework is proposed for deceiving real-world aerial detectors, which shows robust and adaptable attack performance in camera shooting direction agnostic (CSDA) scenarios. The core idea of DGA is to utilize affine transformations to constrain the relative orientation of the patch to the target and introduce three types of loss to reduce target detection confidence, make the color printable, and smooth the patch color. We introduce a direction-guided evaluation methodology to bridge the gap between patch performance in the digital domain and its actual real-world efficacy. Moreover, we establish a drone-based vehicle detection dataset (SJTU-4K), which labels the orientation of the target, to assess the robustness of patches under various shooting altitudes and views. Extensive proportionally scaled and 1:1 experiments are performed in physical scenarios, demonstrating the superiority and potential of the proposed framework for real-world attacks.

A spaceborne bistatic surveillance radar system, which may increase the target radar cross section through a large bistatic angle, can effectively improve the performance of small air moving target indication. However, due to the separation of the transmitter and the receiver, the clutter exhibits severe range dependence in both Doppler and spatial-angle domains. This may significantly decrease the number of independent identically distributed samples and, thus, cause the degradation of clutter suppression performance. In addition, when an air moving target flies above the sea, the broadened spectrum of sea clutter caused by the random intrinsic motion will further influence the clutter suppression performance. To deal with these problems, this article establishes a multichannel sea clutter model for a spaceborne bistatic surveillance radar system. The calculation of the positions of the iso-range contours is analyzed first. Then, the bistatic sea clutter model is established based on the directional wave spectrum, the monostatic–bistatic equivalence theorem, and the two-scale scattering model, where the Earth rotation and the range ambiguity of clutter are considered. Finally, the range dependence and the spectrum characteristics of bistatic sea clutter are discussed, and the clutter suppression performances under different azimuth bistatic angles, different elevation angles, and different sea states are analyzed. In addition, the influence of clutter range ambiguity is analyzed based on the simulations.

To achieve tight beam coverage and obtain the optimal target detection performance for a space-based early warning radar (SBEWR) system, the reasonable radar beam design is prerequisite. In this paper, an optimal beam position design algorithm in a SBEWR system is proposed, where the beam position overlap rate (BPOR) choice is reasonably designed. In the proposed algorithm, the maximum signal-to-clutter and noise ratio (SCNR) of the targets located at the beam position edge is chosen as the criterion for the BPOR optimization design. The BPOR designment result provides important guidance and references for beam filling designment and guarantee the effective target detection capability of a SBEWR system.

Due to existence of the long along-track baseline (ATB) between the satellites in a distributed space-based early warning radar (DSBEWR) system, a large number of grating lobes appear in radar returns, causing the noncontinuous detection phenomenon of an aerial moving target (AMT). To solve this problem, in this paper, a novel approach for ATB distribution design in a muti-satellite DSBEWR system is proposed. In the proposed algorithm, to reduce the amount of spatial ambiguity points located at the main-lobe region, the main-lobe gain of the received antenna pattern in the azimuth dimension is designed to be maximum via the non-uniformly intersatellite ATB designment. The effectiveness of the proposed method is verified by the simulated multi-channel radar data in a DSBEWR system.

Structure-from-Motion (SfM) is the process of estimating 3D scene structure and sensor pose from a set of 2D inputs. In this paper, we present a matrix factorization scheme for solving SAR SfM problem. First, SAR imaging model is linearized at local scene and the SfM problem is converted to a problem that decomposes data matrices into the product of two kinds of matrices, one of which satisfies Stiefel constraint. We then propose a Riemannian conjugate gradient descent algorithm leveraging the Stiefel constraint. SAR SfM is solved by the proposed algorithm via alternating iteratively estimating radar pose and scene structure. Finally, the feasibility and accuracy of the proposed scheme are verified through experiments.

Recently, fully supervised deep learning has achieved excellent success in remote sensing (RS) scene classification and segmentation. However, supervised learning requires tremendous labels, which are difficult to obtain in the field of RS. Self-supervised contrastive methods alleviate this problem by learning impressive transferable representations invariant to different data augmentations, e.g. color jittering. Such invariance could be harmful to RS scene classification and segmentation, which is sensitive to color changes. Therefore, we introduce a color-aware self-supervised learning framework (ColorSelf) for RS scene classification and segmentation. Our model encourages to preserve color-aware information in learned representation to improve their transferability. Extensive experiments on two challenging RS datasets demonstrate the proposed ColorSelf brings a significant performance improvement in both RS scene classification and segmentation task.

As one of the main statistical characteristics of clutter, the clutter amplitude distribution plays an important role in the accurate modeling of spaceborne multi-channel radar signal as well as the subsequent, maritime radar target detection. In this paper, considering that the space-time adaptive processing (STAP) technology is usually applied to accomplish the main-lobe clutter rejection in a space-borne surveillance radar, the influence of different clutter amplitude distributions on STAP in a spaceborne multichannel radar system are analyzed. Firstly, a spaceborne multi-channel clutter model is established based on radar equation and clutter space-time steering vector. Then, Rayleigh distribution, Weibull distribution, lognormal distribution, K distribution, generalized Pareto distribution, and IG-CG distribution are used to fit the clutter amplitude. Finally, the effects of these clutter amplitude distributions on STAP performance are analyzed, respectively. The simulation results show that in the case of the same clutter power, the influence of different clutter amplitude distributions on STAP performance is approximately the same.

The space-based surveillance radar system has a higher field of view and can overcome interference from Earth's atmosphere and terrain occlusion, which has been widely applied in high-threat near-Earth asteroid (NEA) warning and defense applications. Due to the limited power aperture product of the space-based system and the far distance between the radar and asteroid targets, the target signal is extremely weak. Prolonging the coherent accumulation time can effectively improve the radar detection capability of small asteroid targets, but the complex effects of range migration (RM) and Doppler frequency migration (DFM) will degrade the target coherent accumulation performance. To effectively solve this problem, an improved Keystone transform (KT) matched filtering banks method based on particle swarm optimization algorithm is proposed. Compared with traditional methods, the proposed method can not only ensure that the asteroid target detection performance is close to the theoretical optimum, but also reduce the system computation complexity. Simulation results verify the effectiveness of the proposed algorithm.

In this letter, a high-resolution SAR imaging algorithm based on a modified omega-K algorithm is proposed. The proposed algorithm first multiplies the reference signal. Then, the azimuth non-stationary phase error (ANSPE) is considered after performing the interpolation process based on the generalized frequency scale transformation. Finally, a well-focused SAR image can be obtained after compensating the ANSPE and correcting the residual spatial variant range migration by utilizing the range segmentation technique. The simulation results of point targets and the real-measured airborne high-resolution SAR data simultaneously verify the effectiveness and feasibility of the proposed algorithm.

In this letter, the clutter range dependence issue caused by crab angle is investigated in an airborne multi-channel radar system. A new method is proposed to accomplish the crab angle estimation and the non-stationary error compensation. Firstly, from the center-biased and spectrum-broadened characteristics of the clutter space-time spectrum in the case of non-neglected crab angle, a 1-D cost function based on space-time spectrum broadening degree is constructed to achieve the accurate crab angle estimation. Then, the clutter non-stationary errors can be effectively compensated in the post-Doppler domain, significantly improving the subsequent space time adaptive processing (STAP) performance. Simulation results are presented to validate the feasibility and effectiveness of the proposed method.

Most existing works on lightweight SAR ship detectors sacrifice a lot of detection accuracy to reduce model size. In this letter, we propose an ultra-lightweight detector based on distillation technology, which can reduce the parameter quantity of the model while minimizing the damage to the model’s detection accuracy. Due to the scattering interference and speckle noise in SAR images, directly applying the existing ultra-lightweight detectors cannot achieve satisfactory performance for ship detection. As a result, we design a global relationship distillation (GRD) algorithm for the ultra-lightweight SAR ship detector. This algorithm can preserve more global relationships from the teacher and mitigate the accuracy degradation caused by the noise and interference, especially in complex inshore scenarios. Besides, the features learned by this algorithm are robust, and the pruned model is more stable. The superiority of the GRD method over several state-of-the-art distillation methods has been evaluated on the HRSID dataset.

For air moving target indication in nadir region, due to the fact that a spaceborne radar beam can illuminate the top of fuselage, the target radar cross section is usually high, which is beneficial for the detection of a low-observable target. However, due to the short slant range, specular reflection effect, and relatively low radar ground resolution, the power of clutter component from nadir region is comparatively high, leading to the insufficient clutter suppression and the degradation of target detection performance. Fortunately, when an air moving target is adequately high, the target echo is able to be separated from the main clutter echoes due to a shorter time delay, making it possible to be only mixed with low-power ambiguous clutter echoes. Based on these considerations, this paper analyzes the performance of air moving target indication in nadir region for a spaceborne surveillance radar system. It analyzes the target minimum detectable velocities with different target heights and beam center elevation angles. Also, an effective sample selection method based on adaptive range segmentation is proposed to solve the power heterogeneity issue between the main clutter area and the range ambiguous clutter area. As a conclusion, the larger the elevation angle of an air moving target is, the higher the minimum target detectable height is.

Synthetic aperture radar (SAR) is an advanced remote sensing technique, capable of observing Earth’s surface independent of weather conditions and sunlight illumination. Restricted by the minimum antenna area, however, conventional spaceborne SAR systems cannot achieve high azimuth resolution in a wide swath. In addition, blind ranges are present as the constant pulse repetition interval (PRI) is used. To solve these problems, a PRI-staggered elevation multichannel SAR (EMC-SAR) system is employed in this article. By transmitting the continuously PRI-varied sequence, the blind ranges are located at different regions in different receive instants, effectively avoiding the loss of coverage in elevation. In this system, three issues are required to be addressed: 1) recovering the missed data located at blind ranges; 2) suppressing range ambiguous components; and 3) restoring the PRI-varied signal into a regular grid. To deal with these problems, we propose a novel SAR imaging method for a PRI-staggered EMC-SAR system. To be applied on-ground, assume downlinking of the individual elevation channels. First, the modified$\varepsilon $-insensitive loss tube regression with the L2 regularization method is applied to recover the missed data. Then, the range ambiguous components are suppressed by performing digital beamforming (DBF) based on the elevation multichannel technique, where the covariance matrix is constructed by using an iterative adaptive algorithm. After that, a generalized scaling transform is employed to restore the PRI-varied signal into a uniform sampled grid. Finally, a well-focused SAR image can be obtained by performing the conventional SAR imaging techniques. The effectiveness of the proposed method is validated by both simulated and real SAR data processing results.

Multiple-input–multiple-output (MIMO) technique has recently received great attention due to its promising prospect for ground moving target indication (GMTI) applications in a synthetic aperture radar (SAR) system. In this article, an SAR-GMTI processing technique in an MIMO-SAR system with dual-channel configuration is proposed. In the proposed method, up and down chirps are adopted as the transmitted waveforms. Then, the baseline estimation and clutter rejection are simultaneously accomplished in the raw Doppler data domain, after which two separated range-compressed echo signals corresponding to two transmitters can be obtained without the influence of cross-correlated interferences of the clutters. Subsequently, a ground moving target can be detected, clustered, finely refocused based on the Doppler chirp parameter estimation, and accurately relocated by using the along-track interferometry (ATI) processing technique. Simulated experiments are implemented to validate and evaluate the feasibility of the proposed method.

Due to the existence of the time synchronization error, the correlation coefficient between the main satellite and auxiliary satellite severely declines, thus deteriorating the clutter suppression ability and aerial moving target indication (AMTI) performance in a distributed space-based early warning radar (DSBEWR) system. In this paper, a novel algorithm is proposed to estimate and calibrate the linear time synchronization error. In the proposed algorithm, the rough linear time synchronization error range is firstly estimated according to the Doppler offset between the same spatial channel in the main satellite and the auxiliary satellite. After obtaining the searched rough error range, the linear time synchronization error can be accurately obtained by finding the max correlation coefficient between the main satellite and auxiliary satellite after performing time synchronization error compensation. The effectiveness of the proposed method is verified by the simulated multi-channel radar data in a DSBEWR system.

Due to existence of the long along-track baseline (ATB) in a distributed space-based early warning radar (DSBEWR) system, a large number of grating lobes appears in both main-lobe and side-lobe clutter regions, causing the discontinuously detectable phenomenon of an aerial moving target (AMT). To solve this problem, in this paper, a novel spatial ambiguity suppression algorithm is proposed. In the proposed algorithm, based on the prior information provided by the system parameters and the signal processing results from the single satellite radar data, after performing the multi-channel clutter rejection, the spatial ambiguity can be eliminated via the joint processing of residual clutter signals corresponding to the single satellite and the muti-satellites. Simulated processing results are provided to validate the effectiveness of the proposed method.

In this paper, we propose a novel dictionary learning model based on the idea of deep unrolling to deal with the synthetic aperture radar (SAR) image change detection problem. Deep unrolling aims at unrolling the iterative algorithm into a trainable neural network. In our proposed method, the idea of unrolling is applied to the Iterative Shrinkage Threshold Algorithm (ISTA), which is one of classic algorithms for dictionary learning. Then, the proposed Unrolling Iterative Shrinkage Threshold Algorithm (UR-ISTA), is utilized to obtain the sparse codes of the difference results. Finally, the change map is computed by k-means clustering algorithm. The advantage of UR-ISTA method is relatively low time cost, which makes it possible to add dictionary updating step to calculate specific feature vectors. Experimental results show that the proposed approach has superior accuracy and precision compared to several well-known change detection techniques. The proposed UR-ISTA algorithm shows more robustness than another sparse representation algorithm.

For reducing computational burden of electronic hardware and increasing practical recognition performance of optron-ic convolutional network (OPCNN), here we propose an optical backpropagation algorithm and realize the in-situ training OPCNN in optical platform for SAR target recognition. Training networks according proposed algorithm, major computational operations in forward and backward propagating process are all executed in optics with the speed of light and low consumption. Several experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the feasibility of proposed in-situ training algorithm.

Geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO-BiSAR) has the advantages of wide beam coverage, long exposure time, and superior system flexibility. However, it is a challenge for GEO-BiSAR to efficiently acquire SAR images both with high resolution and wide swath due to the severe range and azimuth spatial variances. To deal with this issue, a modified Omega-K method is proposed in this paper. In the proposed algorithm, an equivalent range model associated with GEO-BiSAR configuration is built, where the equivalent parameters of the airborne radar receiver absorb the phase parameters corresponding to the GEO transmitter. Then, the Stolt interpolation is performed to realize the range-azimuth decoupling, achieving the linearization between range and frequency variables. Finally, a well-focused SAR image could be obtained after residual spatial variance error compensation. Simulations are presented to demonstrate the effectiveness of the proposed method.

A working video synthetic aperture radar (VideoSAR) can obtain continuous observations of a region of interest from different viewpoints, meaning those observations naturally contain 3D information for positioning. This paper reports our preliminary studies on SAR scene structure-from-motion using several frames extracted from a VideoSAR sequence. In contrast to classic stereo-radargrammetric workflow, the radar pose is estimated only with information measured by radar. The coordinates of each scattering point are calculated by least-square optimization. We also develop an affine transformation aware dense matching framework to accurately measure pixel-level correspondence between frames. Experiments on real VideoSAR data demonstrate the validity of our approach.

By transmitting the constant pulse repetition interval (PRI) sequence, the traditional synthetic aperture radar (SAR) system will suffer from the loss of coverage in elevation. Thus a staggered-SAR system is developed in recent years, which employs the continuously PRI-varied sequence, making the blind ranges locate at different regions in different receive instant. However, the nonuniformly sampled signal will cause ambiguities in the SAR imaging result. To deal with this issue, in this paper, a novel signal restoration method based on kernel regression is proposed to resample the nonuniform signal. Real-measured spaceborne SAR data is used to validate the proposed method.

Next-generation SAR imaging system demands wide-swath and high resolution to observe Earth's surface, promoting the development of the high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system working in terrain observation by progressive scans (TOPS). However, Doppler ambiguities will occur in this system, causing the imaging performance severely degrading. To address this issue, in this paper, a novel Doppler ambiguous suppression method for an HRWS-TOPS SAR system based on the orthogonal projection subspace is developed. The effectiveness of the proposed method is verified by both the simulated and real SAR data.

In this paper, a novel visualization method for GPR data interpretation and target analysis is proposed for underground target detection and recognition. After preprocessing, the original B-scan image has been transformed into two binary images containing potential targets. Then in the target extraction part, a novel row connection clustering algorithm is applied to separate all possible hyperbolic regions. Finally, a neural network is used to analyze the extracted slices and to estimate the parameters including the material and size of the targets. To illustrate the performance of the proposed method, this paper uses synthetic data generated from gprMax, which demonstrates the exciting accuracy of target detection and recognition.

Due to the atmospheric turbulence, defocusing, noise and other factors, the optical remote sensing image acquisition may become blurred. Therefore, it is critical of deblurring the images by algorithm. In recent years, neural network algorithms have shown excellent performance in optical re-mote sensing images deblurring. However, neural network algorithms have some limitations at the same time. They lack interpretability and need large amounts of training samples. The traditional deblurring algorithms are interpretable, but the performance is not as good as the neural network algorithms. In order to obtain an interpretable deblurring algorithm with good performance, this paper proposes a deblurring algorithm based on deep unfolding method, which is the combination of traditional algorithms and neural networks. It can achieve good performance and be interpretable at the same time. We demonstrate the effectiveness of the algorithm on remote sensing datasets with PSNR values and visual deblurring images. The experiments show the proposed algorithm has better deblurring results.

This paper presents a multi-structure extraction kernel dictionary learning (MSEK-DL) method for synthetic aperture radar (SAR) automatic target recognition (ATR). In order to extract the multi-structure features of SAR images for data enhancement and noise suppression, a matrix approximation method is used. Instead of using traditional linear dictionary learning method, non-linear kernel function is used to map the targets into a high-dimensional space, in order to obtain a better classification performance. The training method and optimization steps of MSEK-DL are presented in this paper. We carried out the experiment based on MSTAR dataset to demonstrate the effectiveness of the proposed classification algorithm. The experimental results show that the classifi-cation algorithm has better classification performance than some representative dictionary learning algorithms, espe-cially for small training datasets.

Imaging through scattering media is widely used in many research fields ranging from astronomical imaging to biomedical imaging. Here, we abstract the scattering media as a modulation model combining phase modulation and optical Fourier transform. Through simulation and experiments, the rationality of the model has been verified. Based on the modulation model, we can describe the scattering process with a mathematical model. From the mathematical model, we conclude that the speckle autocorrelation is the Fourier amplitude of the target image. In other words, by collecting the speckle pattern in the experiment and performing autocorrelation calculation on it, we can obtain the Fourier amplitude of the target image. However, the target image cannot be restored based on the Fourier amplitude information alone. The phase recovery algorithm can be used to recover the Fourier phase of the target image. In our experiment, the speckle autocorrelation results are consistent with the proposed model, and the target image is reconstructed successfully.

The marine moving target detection is a very attractive research field in recent years. However, due to the existence of sea clutter with complex internal motion, the traditional space-time adaptive processing (STAP) methods may not obtain good processing performance. In this paper, the multichannel sea clutter is modeled and the time-varying and space-varying properties of sea clutter are analyzed. In addition, the time-frequency distribution characteristics of sea clutter are analyzed. Finally, a multichannel sea clutter suppression method based on short-time Fourier transform is proposed. The simulation results show that the proposed method can achieve better sea clutter suppression performance than that of the traditional STAP method.

With the development of deep learning, SAR target rotating detection has become a research hotspot. However, the regression process of the rotated boxes is challenging, especially for the rotating detection in target gathering areas such as docks and airports. This is because the regions of interest corresponding to adjacent targets have a relatively large overlap, which may cause data misalignment during the regression and classification process. To solve this problem, we propose a three-stage cascaded rotating regression network (3SCR2Net). Specifically, a horizontal anchor will be rotated and corrected three times continuously. 3SCR2Net overcomes the original problem of information misalignment caused by rotation detection in a complex background. In addition, two decoders are designed in this paper to decode the regression parameters. Experimental results on the SSDD+ dataset demonstrate that the proposed network outperforms five state-of-the-art methods in mean average accuracy (mAP).

A growing number of researchers have begun to use arbitrary-oriented detectors to detect ships. Because in the remote sensing dataset, ships are shown to be narrow and dense. Most of the deep learning methods used in synthetic aperture radar (SAR) ship detection are the same as or variants of those of optical remote sensing. However, the experimental settings of different papers are different. Therefore, various arbitrary-oriented target detectors can not be compared fairly on the SAR dataset. To solve this problem, we developed an arbitrary-oriented SAR ship detection benchmark, which provides strong baselines and state-of-the-art methods in rotation detection. All benchmark methods are tested on RSSDD datasets, and the code is publicly released at https://github.com/open-mmlab/mmrotate. Meanwhile, this paper further explores the role of ImageNet pretrained weight in SAR target detection and tries to train the SAR pretrained weight through unsupervised learning.

We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning. MMRotate implements 18 state-of-the-art algorithms and supports the three most frequently used angle definition methods. To facilitate future research and industrial applications of rotated object detection-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of rotated object detection. MMRotate is publicly released at https://github.com/open-mmlab/mmrotate.

In this letter, a novel method is proposed to suppress the terrain fluctuation clutter based on adaptive broadening processing. In the proposed algorithm, the flat interference phase is first compensated according to the priori radar system parameters. Then, according to the space-time trajectory distribution of clutter edge, the level of clutter Doppler spread in virtue of crab effect is estimated by a cost function related to the clutter eigenvector matrix. Finally, after calculating the clutter suppression weight vector according to the broadened clutter subspace, the non-stationary ground clutter can be robustly rejected. The validity of the proposed method is verified by both the simulated and real-measured multichannel radar data.

The spaceborne high-resolution wide-swath synthetic aperture radar (HRWS-SAR) system generally does not meet the optimal SAR imaging configuration, and thus, it is necessary to apply digital beam-forming filtering technology to restore the nonuniformly sampled signal into the uniform grids. However, in practice, because of the influences of temperature, receiving machine, and other error factors, the channel errors may possibly exist, causing the SAR image to be smeared. To address this issue, this letter proposes a novel algorithm based on the hybrid artificial fish school algorithm–glowworm swarm optimization–genetic algorithm (AFSA-GSO-GA) to address the channel imbalance issue. First, coarse HRWS-SAR imaging processing is performed to obtain the positions of the Doppler ambiguity components. Then, according to the designed cost function, the hybrid AFSA-GSO-GA algorithm is used to realize the channel phase error estimation. Finally, a well-focused SAR image can be obtained after channel balance. The effectiveness of the proposed method is validated by both simulated and real SAR data.

The rapid development of the modern electronic counter-countermeasures (ECCMs) has made the conventional inverse synthetic aperture radar (ISAR) associated with the regular signal waveforms more vulnerable and unreliable. To deal with this issue, the complex waveform designment with staggered pulse sampling is developed in an ISAR system in this letter. In the proposed method, the generalized time-scaled transform (GTST) is adopted to effectively accomplish the irregular signal reconstruction and linear phase decoupling. After that, a set of matched filtering functions are established and interspersed in the imaging processes to accomplish the subsequent motion compensation, target imaging, and range and cross-range scaling. Finally, a satisfied ISAR imagery corresponding to the real size of a moving target can be recovered. The simulation and real-measured radar data processing results are applied to demonstrate the effectiveness of the proposed method.

Sea clutter magnitude distribution exhibits important guiding significance for the design of marine target detection algorithms and the selection of the constant false alarm rate (CFAR) detection threshold. In this letter, to deal with the limitation of the traditional generalized-K (GK) distribution in highly heterogeneous clutter scene, a modified GK (MGK) distribution is proposed for sea clutter magnitude. By combining the traditional GK and generalized Pareto distributions, the value range of power parameter and the applicable range of the distribution model are extended. The applicability of the proposed distribution model is verified by real-measured sea clutter data.

Single image super-resolution technology is critical in remote sensing fields because it can effectively improve the details of target images. However, the application of deep learning is limited due to the lack of interpretability and the need for many parameters. This letter proposes an interpretable dual-branch multi-scale channel fusion unfolding network (DMUNet) for optical remote sensing image (ORSI) super-resolution. We design an unfolding network with double branches, each optimized with different strategies. Two branches focus on texture and edge reconstruction, respectively. This unfolding network follows the iteration process of the alternating direction method of multipliers (ADMM) and can learn the hyper-parameters adaptively. The functions of the two branches can complement each other. Further, to better fuse the feature maps of the two branches, a multi-scale fusion module is proposed. This module can effectively fuse information between different branches, scales, and channels. It is noted that it only requires a little computation cost. Experiments on two public ORSI datasets demonstrate that our method can achieve significant performance in both quantitative evaluation and visual results.

Single image super-resolution technology is critical in remote sensing, effectively improving the resolution of target images, with super-resolution algorithms based on deep learning demonstrating superior performance. However, most neural networks present shortcomings, such as lack of interpretability and requiring a long training time, limiting them in some application scenarios. Moreover, due to multi-degradation factors, tasks put forward higher requirements for the adaptability of algorithms. Therefore, this work develops a structured deep unfolding network (SDUNet), which is adaptable and requires a lower training time by cascading multiple small network modules. Additionally, the unfolding strategy proposed deals with multiple degradations, fully exploiting prior knowledge. The suggested method is challenged against state-of-the-art neural network methods on one optical remote sensing image dataset and one natural image dataset. The experimental results demonstrate our method’s effectiveness in requiring less training time, involving fewer parameters, and achieving a higher reconstruction performance for optical remote sensing image super-resolution.

Single image super-resolution technology based on deep learning is widely applied in remote sensing. In recent years, the deep unfolding super-resolution strategy has been proposed, which combines the neural networks with traditional optimization-based algorithms, making the neural networks interpretable and achieving high performance. However, the typical deep unfolding algorithms usually treat different kinds of blurring kernels in the same way, so the algorithms cannot take advantage of the properties of blurring kernels, limiting the algorithm’s performance. To design a super-resolution network that can fully use the properties of Gaussian blurring kernels, a dual-resolution local attention unfolding network (DLANet) is proposed. Based on the Gaussian blurring functions, a low-resolution (LR) space branch is designed to supplement the high-resolution (HR) space branch. Specifically, for Gaussian blurring kernels, the closer the pixel is to the center, the greater the weight is. It means that the pixel points retained after downsampling will contain more information about the original corresponding pixel points, and it could be easier to estimate their original pixel values. So we design two branches. The HR branch completes the estimation of the whole image, and the LR branch only estimates the points retained after downsampling. To better complete the feature fusion of the two branches, we propose a row-column decoupling local attention module. This module can retain more information when fuse features and the row-column decoupling strategy can reduce computational complexity. Comprehensive experiments demonstrate the superiority of our method over the current state-of-the-art on remote sensing datasets.

In this letter, we propose a novel range and Doppler sampling mismatch compensation method for moving target detection, which can effectively improve the output signal-to-noise ratio (SNR) of a moving target. In the proposed method, after performing the target coherent integration by using the well-known Keystone transform (KT), the range and Doppler sampling mismatch errors (SMEs) are estimated and compensated based on the constructed optimization model with the consideration of the change rate of a moving target peak amplitude. In order to improve the computational efficiency, the Hooke–Jeeves method is applied to achieve the optimal solution of the constructed optimization problem, thus efficiently solving the target energy diffusion problem caused by the SMEs. Simulated experiment is presented to verify the effectiveness and feasibility of the proposed method.

In this paper, we propose a novel long-time coherent integration detection method to detect an uncooperative moving target in a frequency and pulse repetition interval randomly jittering radar system based on signum coding (SC). In the proposed algorithm, an additional reference waveform is applied to eliminate the third-order harmonic influence induced by SC. Then, a generalized Keystone transform (GKT) is proposed to resolve the complex coupling among the range frequency, jittered carrier frequency, and nonuniformly sampled time. Simulation results are presented to validate the effectiveness and feasibility of the proposed method.

Due to the limited size, carrying capacity, power-aperture product, and high hardware cost of satellite platform, the traditional single-platform spaceborne radar system encounters the problems of poor target minimum detectable velocity (MDV) performance, considerably deteriorating the moving target detection performance. To improve the air moving target indication (AMTI) performance, especially for a weak target, distributed space-based radar system (DSBR) becomes a good candidate due to the longer along-track baseline (ATB) and spatial power synthesis. However, due to the sparse configuration of radar baseline distribution, the detection performance of air moving targets (AMTs) will be restricted by many practical factors in an actual DSBR system. In this paper, multi-channel signal models of an observed moving target and ground clutter are accurately established in a DSBR framework, where the error influences of cross-track baseline (CTB), terrain fluctuation, and channel inconsistency response are considered. Then, the influence of the non-ideal factors, including the channel noise, long-intersatellite ATB, long-intersatellite CTB, synchronization errors, and interchannel amplitude and phase inconsistency errors, on the AMTI performance is analyzed term by term. The simulation results provide the useful guidance for the system design of a DSBR with the AMTI tasks.

For a spaceborne high-resolution and wide-swath synthetic aperture radar (HRWS-SAR) system, it usually uses the digital beamforming technology. However, in practice, because of the influences of temperature, antenna pattern, receiving antenna, and other error factors, there may exist the range synchronization time errors, amplitude errors, and phase errors among different spatial channels. These nonideal factors will significantly degrade the multichannel data reconstruction performance, resulting in a smeared SAR image. To address this issue, in this article we propose a novel channel error correction algorithm based on the orthogonal projection theory. First, the optimal weight of each Doppler ambiguity component is calculated by the orthogonal projection. Then, the cost function is constructed based on the power maximization criterion, from which the channel phase errors can be obtained. Finally, the HRWS-SAR imaging can be finely realized after performing the channel balancing. Compared with the conventional phase error estimation method, the proposed algorithm does not require to perform the matrix eigenvalue decomposition, avoiding the signal leakage phenomenon under low SNR case. The effectiveness of the proposed algorithm is validated by both airborne and space-borne real SAR data.

In a multichannel synthetic aperture radar (SAR) system, because of the target uncooperative motion, a ground moving target (GMT) is usually smeared, distorted, and shifted in an SAR image. In this article, a novel approach for multichannel SAR-GMT indication (GMTI) processing is proposed. The main innovations of this method are that a GMT can be well refocused and relocated since the target high-order Doppler parameters can be precisely estimated based on a high-order polynomial phase signal (PPS) model, and the target statistical amplitude and phase information is jointly applied to improve the radial velocity estimation robustness. Compared with the current SAR-GMTI algorithms, the improvements of this method over the existing methods are: 1) the topography interferometric phase can be effectively compensated by applying an adaptive 2-D spectrum filtering technique via iterative processing; 2) a GMT can be well imaged since the target Doppler chirp rate and the quadratic chirp rate can be well estimated via the 2-D coherent integration in the time–frequency plane; and 3) a GMT can be precisely relocated into its original position by applying the generalized amplitude and phase weighting technique. Real-measured SAR data processing results are presented to validate the effectiveness and feasibility of the proposed method.

In inverse synthetic aperture radar (ISAR) imaging for a uniformly moving rigid-body target, a finely focused ISAR image can be obtained by using the conventional range-Doppler algorithm. However, the ISAR image quality may significantly deteriorate when the time-vary Doppler phases in virtue of target maneuvering motions are present, such as an airplane with nonuniformly rotation and a ship with fluctuation. This has become a challenging task, especially under nonhigh signal-to-noise ratio (SNR) environment. In this article, a novel ISAR imaging algorithm for a maneuvering target with moderate reflection intensity is proposed. After motion compensation, the radar echo signal in a range cell is modeled as a multicomponent cubic phase signal (CPS), in which the chirp rate and the quadratic chirp rate are two important physical quantities that may determine the target ISAR focusing quality. Based on a symmetrical instantaneous autocorrelation function, the received CPSs are transformed into the time and lag-time plane, and then a 2-D coherent integration can be realized after the generalized time-scaled transform and 1-D maximization. This forms a high-quality ISAR image. The effectiveness and superiority of the proposed algorithm are validated by the ISAR imaging results of simulated and real measured data.

Due to the high-speed movement of a spaceborne radar (SBR) platform, the geographic clutter spectrum expands severely, resulting in the useful moving target signal submerged by the main-lobe clutter background. To deal with this issue, the equipped multichannel arrays in an SBR system provide sufficient spatial degrees, and as a consequence, the space-time adaptive processing (STAP) technology is often preferred to achieve the moving target detection, even in the main-lobe clutter regions. However, for the moving target detection under the sea scene, due to the complex internal motion of sea clutter, the clutter signal received by an SBR system may possess the space- and time-varying characteristics, worsening the multichannel clutter rejection performance using the traditional STAP techniques. In this article, a novel sea clutter suppression method based on the joint space-time-frequency adaptive filtering is proposed. In the proposed algorithm, according to the coherent time analysis of sea clutter, the subaperture time-domain sliding window is employed to alleviate the clutter decorrelation effect, and then, a modified subspace projection technique is applied to accomplish the first-stage clutter rejection. After realizing the effective signal recovery with respect to these residual subaperture clutter data, the second-stage spatial filtering method is applied to realize the final clutter suppression with respect to the relatively high Doppler resolution clutter returns. The effectiveness of the proposed algorithm is verified by both simulated multichannel sea clutter data and real-measured sea clutter data.

When an early warning radar installed in a spaceborne platform works in a down-looking mode to detect a low-altitude flying target, the severely broadened main-lobe clutter cannot be ignored, which will cause the deterioration of the moving target detection capability. To deal with this problem, a space–time adaptive processing (STAP) technique is proposed for effective clutter suppression based on the spatial–temporal 2-D joint filtering. However, the full-dimensional optimal STAP encounters the challenges of high computational complexity and large training sample requirement. Therefore, the dimension-reduced STAP technique becomes necessary. This article proposes a novel dimension-reduced STAP algorithm based on spatial–temporal 2-D sliding window processing. First, several sets of spatial–temporal data are obtained by using spatial–temporal 2-D sliding window. Then, for each set of data, the 2-D discrete Fourier transform is performed to transform the echo data into the angle-Doppler domain. Finally, jointly adaptive processing is performed to realize the clutter suppression. Compared with the conventional STAP algorithms, the improvements of this method over the existing methods are: 1) the proposed method requires fewer training samples due to the 2-D localization processing and 2) the proposed method can obtain the better clutter suppression performance with lower computational complexity. The feasibility and effectiveness of the proposed algorithm are verified by both simulated and real-measured multichannel surveillance radar data.

High speed, high maneuverability, and weak space-moving targets (SMTs) are major threats for space-borne radar (SBR) systems. First, the severe range migration (RM) and Doppler extension are induced by complex relative motion between the radar platform and non-cooperative moving targets, making moving target detection (MTD), and parameter estimation particularly difficult. Apart from this challenge, Doppler ambiguity and Doppler aliasing arise from the limited pulse repetition frequency (PRF) of a SBR system to ensure an adequate coverage rate, which may make the existing MTD algorithms deteriorate dramatically. To address these issues, we focus on the detection of high speed and high maneuverability targets based on the modified keystone transform matched filtering (MKTMF), whose Doppler frequency exceeds PRF as well as spans multiple PRFs. The proposed method is suitable for the weak MTD under a low signal-to-noise ratio (SNR) case since the nonlinear operation is not involved. Finally, some numerical results and real data results are provided to validate the superiority of the proposed method.

In this paper, a precise beam position design algorithm for space-based early warning radar (SEWR) based on the actual antenna pattern search is proposed. In the proposed algorithm, the beam positions along elevation and azimuth dimensions are filled according to the main lobe widths of elevation and azimuth antenna patterns, respectively, and thus the beam position designment is more accurate since the range and azimuth angle dependences are considered. Based on the designed beam positions, the precise dwell time can be obtained according to the coverage rata of a SEWR system. The beam position designment results by using the proposed method provide a reference for the beam filling designment of a space-based radar.

Using deep convolutional neural networks to achieve automatic target recognition (ATR) is effective but it will bring heavy computation burden. Here we propose an optronic convolutional neural network (OPCNN) to realize ATR in optics. By using OPCNN, the computation cost is dramatically reduced and good performance of recognition accuracy is obtained simultaneously. Experimental results on Moving and Stationary Target Acquisition and Recognition dataset demonstrate the feasibility of our proposed OPCNN architecture.

The azimuth multichannel spaceborne SAR system can achieve the high-resolution and wide-swath simultaneously. In a high-resolution and wide swath (HRWS) SAR system, the Doppler centroid (DC) frequency estimation is an important step for Doppler ambiguity signal reconstruction, which should be precisely estimated. In this paper, we propose a novel Doppler centroid frequency estimation method for HRWS-SAR system based on modified shuffled frog leafing algorithm (MSFLA). Firstly, the cost function is designed based on the average power difference between ambiguities and useful signal. Then, the MLSFA algorithm is proposed to accomplish the DC estimation. Finally, a good HRWS-SAR image is obtained based on the estimated DC. Compared with the traditional time-domain based DC estimation methods, the proposed method is not affected by the troubled cross-terms induced by high-order moments. The effectiveness of the proposed method is verified by the simulation experiments and real-measured SAR data.

Moving targets can be detected in SAR images using the difference between two looks because, in the two looks, the images of a stationary target are similar, but the images of a moving target are different. However, the sidelobes in the two looks deteriorate the similarity of the stationary ground between the two looks and thus degrade the performance of this method. In this paper, a scaled Hanning window is designed to generate the two looks with lower sidelobes. The shape parameter of the window is automatically chosen according to the spread of the Doppler spectrum. The results show that this method has a better performance in the suppression of stationary targets than the original method.

The along-track interferometry (ATI) uses the phase difference between the images from different channels to detect moving targets in multi-channel SAR systems. However, the phase differences of weak clutters have large variances, and this may cause false alarms. This paper presents an improved ATI method. In this method, the phase difference is weighted by the normalized interferometric amplitude to detect moving targets. Since the phase differences of weak clutters are suppressed, the false alarm rate is decreased. The results of real data show the effectiveness of this algorithm.

Radar forward-looking imaging has important application value in target attack and terrain detection. However, traditional synthetic aperture radar (SAR) cannot perform high-resolution imaging of the area directly in front of the flight trajectory, due to the limitation of mechanism. This paper proposes a new airborne forward-looking imaging scheme based on maneuvering motion. The scheme firstly starts from the movement trajectory of the platform, gives the spatial geometric relationship of the forward -looking imaging, and establishes the echo signal model, further analyzes the azimuth resolution characteristics of the signal. On this basis, combined with the phase characteristics of the echo signal, a forward-looking imaging algorithm suitable for this scheme is derived. Finally, through simulation experiments, the target imaging results are analyzed to verify the effectiveness of the algorithm.

In this paper, a convolutional neural network based hierarchical kernel dictionary learning, which consists of convolutional neural networks (CNN) and dictionary learning (DL) parts, is proposed for synthetic aperture radar (SAR) target recognition. Compared with conventional DL methods, which use the raw images for training, the CNN part with three convolution layers is utilized to extract the SAR image's hierarchical features. The hierarchical features are introduced into the objective function of DL part. To handle the resulting nonlinear problem, we utilize a nonlinear mapping function to map the dimension-reduced hierarchical features into a higher Hilbert space and perform DL in the space such that the features can be represented linearly. A classification error term is added into the objective function to train a linear classifier. We use the kernel trick to solve the optimization problem. Experiments performed on the MSTAR dataset show that the proposed method outperforms the representative DL methods.

Multichannel Terrain Observation by Progressive Scans (TOPS) SAR has overcomed the system-inherent limitation of conventional synthetic aperture radar (SAR). Multichannel TOPS SAR is capable of imaging a scene with high geometric resolution and wide swath. But the complexity of algorithms to process multichannel TOPS raw data has increased. Multichannel TOPS takes much more time and computer resource to focus an image than conventional SAR does. On the other hand, optical computing has the advantages of high speed, huge capacity and low power. So we tried to use optical computing, especially optical two dimensional Fourier transform, to process multichannel TOPS raw data in this paper. Due to the limitation of system devices, we just partly realized the computing on a 4-f system. In our method, the raw data was firstly compensated to a signal which could be seen as the raw data of another Stripmap system without range cell migration. In the 4-f system, the Stripmap signal is then transformed into its two dimensional frequency domain by a lens and multiplied by a filter to focus the image. We had tested our system with simulation point target signals. The result showed that the proposed method could quickly process Multichannel TOPS data with resolution loss smaller than 11%.

Narrow-band interference (NBI) is a major threat for synthetic aperture radar (SAR) system, which degrades the imaging quality severely. In this paper, a new type of narrow-band interference (NBI) is investigated, i.e., periodicity transmitted NBI (PT -NBI). Compared with the conventional NBI, PT -NBI is intermittently emerged in one azimuth pulse, which shows periodic property in the range time domain and broadband characteristic in the range frequency domain, resulting in the severe performance degradation by using the existing NBI suppression methods. The distinctions and characteristics between the NBI and PT -NBI in different transform domains are analyzed firstly. On this basis, a PT -NBI suppression method is proposed by applying the joint time domain detection and Eigensubspace (ESP) filtering in this paper, which can effectively realize the interference suppression for PT - NBI.

Ground Penetrating Radar (GPR) is a non-destructive technique that employs electromagnetic waves to map subsurface structures. An effective simulation method is essential for GPR system design and signal processing. In this paper, a simulation method of GPR is proposed for dispersive media that is usually encountered in GPR applications. The behavior of dispersive media is simulated in our method by filtering the transmitted signal of GPR with an approximation of the system transfer function that is obtained by interpolating the values of the system transfer function evaluated at different frequencies, each of which is acquired by simulating GPR using the existing ray tracing based method of GPR simulation for non-dispersive media with a mono-frequency signal of the corresponding frequency as its transmitted signal. The accuracy and efficiency of our method were validated by comparing the experimental results of our method to those of the Frequency-Dependent Finite-Difference Time-Domain (FD-FDTD) method. The experimental results demonstrate that our method can simulate GPR for dispersive media with acceptable accuracy consuming much less running time than the FD-FDTD method for GPR simulation for dispersive media.

A growing number of researchers begin to use the arbitrary-oriented detector to detect ships. Because in the remote sensing dataset, ships are shown to be narrow and dense. Most of the deep learning methods used in synthetic aperture radar (SAR) ship detection are the same as or variants of those of optical remote sensing. However, due to the relatively low resolution and signal-to-noise ratio, the amplitude information in SAR image becomes more contaminated than that in optical image. Therefore, it is very difficult to train the arbitrary-oriented target detection task based on SAR dataset. In order to solve this problem, we attempt to further extract the frequency domain features of the SAR image by introducing a frequency attention module. The discrete cosine transform (DCT) to converts SAR image from spatial domain to frequency domain. Experimental results on HRSID datasets show that the proposed method can significantly improve the performance of arbitrary-oriented SAR ship detection.

This letter deals with the motion parameter estimation and focusing for ground moving targets in synthetic aperture radar (SAR) imagery. In the proposed algorithm, after range compression, the echo signal of a moving target is first transformed into the range-frequency and Doppler domain. Then, the target signal is characterized as an inclined trajectory after applying the correlation operation with respect to the divided Doppler subaperture data. Finally, target motion parameter estimation and focusing can be effectively accomplished based on the Doppler axis rotation transform (DART). The effectiveness of the proposed algorithm is validated by both simulated and real airborne/spaceborne SAR data.

In this letter, a novel method is proposed to estimate the along-track baseline for a multichannel synthetic aperture radar (SAR) system, intended for ground moving target indication (GMTI) applications. First, an adaptive spectrum filtering technique is proposed to compensate the terrain interferometric phase caused by the cross-track baseline and then the ground clutter is rejected by applying the joint-pixel displaced phase center antenna (JPDPCA). After performing the moving target detection, the target radial velocity is estimated according to the azimuth position offset by exploiting the road-aided information. Finally, the along-track baseline is derived based on the subspace projection (SP). The effectiveness of the proposed method is validated by data from the experimental airborne system.

In this letter, a novel convolutional neural network (CNN)-based dictionary learning (DL) method is proposed for synthetic aperture radar (SAR) target recognition. Different from conventional target recognition schemes, which consist of the hand-crafted feature extraction followed by a classifier, the proposed scheme utilizes a well-designed ConvNet as the feature extractor, and it can automatically learn hierarchies of features from the training data set. The outputs of the ConvNet are regarded as multifeature and are used for the following multi-DL. For a classification task, we take into consideration the mean-squared error (MSE) combined with a regularization term as the loss function. As a result, the whole architecture combines the ConvNet and DL as an end-to-end framework. We show the back propagation of the loss and update the variables using the stochastic gradient descent with the momentum method. Experiments performed on the moving and stationary target automatic recognition (MSTAR) data set exhibit that the proposed method outperforms many state-of-the-art DL and CNN methods in terms of recognition performance.

In this article, we propose a multichannel sea clutter model in a spaceborne early warning radar system and analyze the influence of the sea clutter motion characteristics on the space-time adaptive processing (STAP) performance. To establish a multichannel sea clutter model, the 3-D Gerstner wave model is applied to construct the sea surface. Then the Pierson–Moskowitz wave spectrum and the stereo wave observation project (SWOP) directional spectrum are combined to describe the amplitude distribution of waves in different frequencies and directions. At the same time, the two-scale model is applied to obtain the specific backscattering coefficients of sea clutter at different time and positions. In addition, breaking waves are added in sea clutter returns with the form of false targets. Finally, the space-time distribution characteristics of sea clutter in a spaceborne multichannel array system and the influences of sea clutter under different wind speeds and directions on STAP performance are analyzed based on the simulation processing results. Processing results of some real-measured radar data are also exhibited to verify the theoretical analyses.

Matrix and tensor completion aim to recover the incomplete two- and higher-dimensional observations using the low-rank property. Conventional techniques usually minimize the convex surrogate of rank (such as the nuclear norm), which, however, leads to the suboptimal solution for the low-rank recovery. In this paper, we propose a new definition of matrix/tensor logarithmic norm to induce a sparsity-driven surrogate for rank. More importantly, the factor matrix/tensor norm surrogate theorems are derived, which are capable of factoring the norm of large-scale matrix/tensor into those of small-scale matrices/tensors equivalently. Based upon surrogate theorems, we propose two new algorithms called Logarithmic norm Regularized Matrix Factorization (LRMF) and Logarithmic norm Regularized Tensor Factorization (LRTF). These two algorithms incorporate the logarithmic norm regularization with the matrix/tensor factorization and hence achieve more accurate low-rank approximation and high computational efficiency. The resulting optimization problems are solved using the framework of alternating minimization with the proof of convergence. Simulation results on both synthetic and real-world data demonstrate the superior performance of the proposed LRMF and LRTF algorithms over the state-of-the-art algorithms in terms of accuracy and efficiency.

An outlier-resistance phase retrieval algorithm based on alternating direction method of multipliers (ADMM) is devised in this paper. Instead of the widely used least squares criterion that is only optimal for Gaussian noise environment, we adopt the least absolute deviation criterion to enhance the robustness against outliers. Considering both intensity- and amplitude-based observation models, the framework of ADMM is developed to solve the resulting non-differentiable optimization problems. It is demonstrated that the core subproblem of ADMM is the proximity operator of the ℓ1-norm, which can be computed efficiently by soft-thresholding in each iteration. Simulation results are provided to validate the accuracy and efficiency of the proposed approach compared to the existing schemes.

A novel ℓp-greedy pursuit (GP) algorithm for robust matrix completion, i.e., recovering a low-rank matrix from only a subset of its noisy and outlier-contaminated entries, is devised. The ℓp-GP uses the strategy of sequential rank-one update. In each iteration, a rank-one completion is solved by minimizing the ℓp-norm of the residual. Unlike the existing greedy methods that use the principal singular vectors of the residual matrix as the solution to the rank-one completion with the index information of the observed entries being ignored, the ℓp-GP employs alternating minimization to obtain an improved solution by fully exploiting the index information. More importantly, it achieves outlier-robustness by setting p = 1. For p = 1, only computing the weighted medians is involved, which yields that the complexity is near-linear with the number of observations. The low complexity enables the ℓ1-GP to be applicable to very large-scale problems. Simulation results demonstrate the superiority of the ℓp-GP over other approaches.

The deep convolutional neural networks (CNNs) have achieved the state of art performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, these networks often provide sub-optimal recognition results in the case of imbalanced SAR data distribution. In this paper, a synthetic minority class data method for improving imbalanced SAR target recognition using the generative adversarial network (GAN) is proposed. The minority class SAR data is first over-sampled by optimized data augmentation policies from automatic search method, which enlarge the training set for GAN. The progressive growing of GANs (PGGAN) is then trained on these data and generates high quality and diverse minority class SAR data to alleviate imbalanced data distribution. Experimental results on the designed imbalanced distributed Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset indicate that our method can effectively improve the recognition accuracy of minority class by approximately 11.68%.

Target recognition is important in SAR applications. Conventional SAR target recognition is based on digital processing.A computer vision aided optical correlator is proposed in this paper. The method is implemented by the correlation between the SAR images and the target library in the optical domain. Ray tracing is used to generate the efficient and accurate target library, and the target parameters are adjustable. Finally, experiment results validate the proposed method.

In order to overcome the disadvantage that traditional multichannel SAR systems are only sensitive to the radial velocities of moving targets, a variable-boresight mode has been proposed, whose kernel is to detect and estimate the velocity component of the azimuth velocity at two nodes. For purpose of improving the estimation accuracy of the azimuth velocity, we introduce more nodes with different squint angles, so as to obtain more velocity components to carry out the estimation by the least square method. Considering that only few tiny angles can be ignored, while introducing more nodes are likely to bring about nonnegligible squint angles, the previously used signal processing suitable for side-looking mode is modified. Finally, numerical experimental results are provided to verify the effectiveness.

In this paper, a fusion of linear and nonlinear classification errors is introduced into kernel dictionary learning and is applied for SAR target recognition. Different from linear dictionary and classifier learning, we utilize a nonlinear mapping function to map the SAR data into a higher dimensional space for nonlinear reconstruction. Inspired by neural networks, a multilayer nonlinear classification structure combined with a linear classification is introduced into the objective function such that the reconstruction error and the two classification errors are optimized simultaneously. In addition, we also use the Gaussian function to filter the noise in SAR images and perform the principal component analysis (PCA) algorithm to extract the main components of the samples. An optimization method is developed to solve the resulting problem. Experimental results performed on the MSTAR dataset demonstrate that the proposed method outperforms some representative dictionary learning and sparse representation schemes.

Sidelobe suppression is an issue of interest in SAR imaging. In order to obtain low-sidelobe SAR images, various sidelobe suppression methods have been proposed. SVA (Spatially Variant Apodization) method can both maintain image resolution and suppress sidelobes. However, this method assumes that the Doppler centroid is zero and the Doppler bandwidth is close to the pulse repetition frequency (PRF). Actually, when the radar has a radial velocity, the Doppler centroid may deviate from zero and the Doppler bandwidth may be significantly smaller than the PRF. In such case, the method has poor performance in suppressing sidelobes. This new algorithm proposed in this paper not only inherits the advantage that the SVA maintains image resolution, but also considers arbitrary Doppler centroids and bandwidths. The result of simulated and real SAR images indicates the advantage of this new algorithm over the traditional methods.

With the rapid development of deep learning, more and more deep neural networks with strong discrimination have come up. One reason why deep learning models can achieve such good results is the abundant annotation data. However, obtaining such considerable amount of annotation data is costly, especially in the field of synthetic aperture radar (SAR). High-quality SAR dataset cannot be constructed without the support of the specialists and institutes in the related field, which leads to the limited amount of labeled training data of SAR. In order to solve the problem that deep neural networks (DNNs) may face under limited labeled training data, researchers often use data augmentation methods to increase the number of labeled samples to boost model performance. But in fact, a large number of augmented training samples not only introduce extra noise, but also additional training time. In this paper, we introduce the active learning into SAR target recognition, which used to help specialists select the samples that are most worth labeling. Moreover, we propose a data-driven active learning scheme named Ranking Loss Module (RLM), which does not rely on artificially strategies to select samples. In contrast to data augmentation, it can improve the performance of the model while reducing the number of training data samples. Experimental results based on MSTAR dataset demonstrate the superiority of the proposed scheme. Using the RLM, better performance can be achieved with only one third of the full training samples.

With the development of synthetic aperture radar (SAR) system, automatic target recognition (ATR) has attracted wide attention in many decision-making tasks, in which an enhanced feature of SAR image is a powerful tool to improve the recognition accuracy. However, the presence of speckle noise and natural clutter inevitably contaminates SAR images and, thus, degrades image features. In this article, we explicitly address the speckle reduction problem for the circular SAR system, in which the motion of aircraft platform causes continuous angular variations so that different SAR images can be captured with the high interrelationship. By exploiting the underlying low-rank and continuous properties among different SAR images, a method called the ℓp-regularized low-rank and space-angle continuity extraction (ℓp-LSCE) is proposed to suppress the noise and enhance the target feature. Taking into account the interrelationship between SAR images, we arrange the images in a 3-D tensor to investigate the space-angle continuity of the targets. Furthermore, we develop a robust ℓp-regularized scheme to incorporate the low-rank property of targets. Then, the joint optimization problem is solved via the framework of augmented Lagrange multiplier (ALM) with efficient computation of each ALM subproblem. The experimental results of circular SAR data sets of the moving and stationary target acquisition and recognition (MSTAR) and the VideoSAR demonstrate that the proposed method can efficiently despeckle SAR images with well-preserved target features, which is conducive to the improvement of ATR performance.

In this article, a supervised nonlinear dictionary learning (DL) method, called multiscale supervised kernel DL (MSK-DL), is proposed for target recognition in synthetic aperture radar (SAR) images. We use Frost filters with different parameters to extract an SAR image's multiscale features for data augmentation and noise suppression. In order to reduce the computation cost, the dimension of each scale feature is reduced by principal component analysis (PCA). Instead of the widely used linear DL, we learn multiple nonlinear dictionaries to capture the nonlinear structure of data by introducing the dimension-reduced features into the nonlinear reconstruction error terms. A classification model, which is defined as a discriminative classification error term, is learned simultaneously. Hence, the objective function contains the nonlinear reconstruction error terms and a classification error term. Two optimization algorithms, called multiscale supervised kernel K-singular value decomposition (MSK-KSVD) and multiscale supervised incremental kernel DL (MSIK-DL), are proposed to compute the multidictionary and the classifier. Experiments on the moving and stationary target automatic recognition (MSTAR) data set are performed to evaluate the effectiveness of the two proposed algorithms. And the experimental results demonstrate that the proposed scheme outperforms some representative common machine learning strategies, state-of-the-art convolutional neural network (CNN) models and some representative DL methods, especially in terms of its robustness against training set size and noise.

Tensor robust principal component analysis via tensor nuclear norm (TNN) minimization has been recently proposed to recover the low-rank tensor corrupted with sparse noise/outliers. TNN is demonstrated to be a convex surrogate of rank. However, it tends to over-penalize large singular values and thus usually results in biased solutions. To handle this issue, we propose a new definition of tensor logarithmic norm (TLN) as the nonconvex surrogate of rank, which can decrease the penalization on larger singular values and increase that on smaller ones simultaneously to preserve the low-rank structure of a tensor. Then, the strategy of tensor factorization is combined into the minimization of TLN to improve computational performance. To handle impulsive scenarios, we propose a nonconvex 'p-ball projection scheme with 0 < p < 1 instead of the conventional convex scheme with p = 1, which enhances the robustness against outliers. By incorporating the TLN minimization and the 'p-ball projection, we finally propose two low-rank recovery algorithms, whose resulting optimization problems are efficiently solved by the alternating direction method of multipliers (ADMM) with convergence guarantees. The proposed algorithms are applied to the synthetic data recovery and image and video restorations in real-world. Experimental results demonstrate the superior performance of the proposed methods over several state-ofthe- art algorithms in terms of tensor recovery accuracy and computational efficiency.

A phase-only robust minimum dispersion (PO-RMD) beamformer is devised for non-Gaussian signals. The proposed PO-RMD employs a constant-modulus constraint on the weights, which is equivalent to simply phase shifting at each antenna. It adopts the minimum dispersion criterion to utilize the non-Gaussianity of the signals while employing the worst-case constraint to achieve the robustness against model uncertainty. A gradient projection algorithmic framework is developed to solve the resulting nonconvex optimization problem. In order to find a feasible point in the intersection of the constant-modulus and robustness constraint sets, an alternating projection algorithm is devised. More importantly, the closed-form expressions of the projection onto the two sets are derived, respectively. Simulation results demonstrate the effectiveness, accuracy and robustness of the PO-RMD.

Azimuth superresolution for real beam scanning radar aims to recover the high-resolution image from low-resolution echo. Among superresolution techniques, regularization-based methods are widely used, but most existing methods lead to the blurring of scattering targets and thus are difficult to distinguish between close targets. In this paper, we propose to employ the nonconvex ℓp-regularization with 0 <; p <; 1 to achieve the sparsity-driven superresolution, which further enhances the azimuth resolution. Furthermore, the resultant optimization problem is efficiently solved using an unified framework via incorporating different proximity operators. Simulation results validate the accuracy and efficiency of the proposed algorithm.

This paper proposes a method that can enhance the features of synthetic aperture radar images based on the exploitation of intrinsic target structure to improve the performance of automatic target recognition (ATR). We take advantage of the interrelationship between images and arrange them into a three-dimensional tensor. Then, by incorporating the joint low-rank and continuity constraints, the intrinsic target structure is extracted and enhanced with the reasonable suppression of speckle noise. Experiments on the moving and stationary target acquisition and recognition public database demonstrate the high quality of feature enhancement of the proposed algorithm, which efficiently improves the ATR performance.

In processing of large scene synthetic aperture radar (SAR) images, the first step is to split them into tiles in order to reduce the load of computer's computation effort and memory, which is crucial in the follow-up procedures of building radar footprints detection or reconstruction. Compared to the traditional cockamamie gridding method, we propose an automatic sub-images extraction approach based on the density and distance-based (DD) clustering algorithm and the connected-component labeling (CCL) algorithm of the graph theory which can avoid a mass of unnecessary least error finding work. According to our method, the original image can be split without introducing excessive subjective operation, which can remain the primary information of buildings' images efficiently. Meanwhile, automatic area searching reduces manpower effectively.

The existing approaches for synthetic aperture radar (SAR) automatic target recognition (ATR) based on deep neural network models have achieved promising performances. However, they cannot give satisfactory detection results when dealing with challenging scenarios, because the performance is influenced by multiple stages. We propose a simple yet powerful method that implements fast and accurate target recognition in SAR image. The system integrates intermediate steps with a single neural network, which can directly predict object of arbitrary orientations in full images. Comparing to the traditional methods, our system can eliminate the influence of previous stage and components in the process. The proposed method is applied to SAR imagery of (moving and stationary target acquisition and recognition) MSTAR dataset and the simulated data. Experimental results used demonstrate the potential of the developed approach in terms of high accuracy and efficiency.

A novel scheme is proposed for the velocity estimation of ground moving targets in multi-channel synthetic aperture radar (SAR) systems. This scheme exploits the statistics of the interferometric phase. In fact, a moving target consists of multiple point scatterers instead of only one. Therefore, the velocity estimation should be based on the interferometric phase of all the scatterers rather than only one. It is proved that the probability density function of the interferometric phase is a Gaussian function, whose peak corresponds to the radial velocity of the moving target. The peak is estimated via a smoothed histogram of the interferometric phase. Finally, the effectiveness of our proposed scheme is verified by simulated and real airborne SAR data.

In this paper, a novel robust multiscale dictionary learning algorithm is proposed for SAR object recognition. By extracting an SAR image's multiscale features and introducing them into the objective function, there are several reconstruction error terms. In addition, each reconstruction error term is calculated according to the sum of the absolute values of matrix entries instead of the Frobenius norm, and it is more robust against noise. An alternating minimization strategy is proposed to optimize the objective function. Experiments on the MSTAR dataset show that the proposed scheme outperforms some representative dictionary learning methods in terms of recognition performance and robustness against noise, especially under small training set size condition.

A novel real-time optronic system for spectrum awareness in remote sensing is proposed, which provides ultra-broad bandwidth as well as high processing speed. A Mach-Zehnder modulator modulates the unknown RF signal to the light wave generated by a Laser Diode. Then a spatial time conversion device converts a serial optical signal in time domain to parallel optical signals in spatial domain. And thanks to the inherent ability of converging lens to perform two dimensional Fourier transform at the speed of light, the frequency spectrum information can be obtained much more easily and fast compared to conventional electronic wideband techniques by using analog-to-digital conversion. In this paper, principle and system design will be discussed, last but not least, simulation and equivalent experiment will be implemented to verify the performance of our system.

High-resolution images with abundant detailed information are necessary elements for various applications of synthetic aperture radar (SAR). In this paper, a novel super-resolution image reconstruction method based on self-normalizing generative adversarial network (SNGAN) is proposed. Compared with other published GAN-based super-resolution algorithms, the proposed method reflects its superiority in two aspects. First, the scaled exponential linear units (SeLU) is introduced as the activation function of generator to give the GAN system self-normalization ability and make it more suitable for SAR images. Second, the batch normalization layers after convolution are canceled to reduce the computational requirement and model oscillation. Experiment results on the images of TerraSAR and MSTAR dataset demonstrate that the proposed method acquires satisfactory performance on the resolution enhancement and target recognition of SAR images.

This letter addresses the coherent integration problem for the moving target detection in a frequency agile radar system. Due to the random phase fluctuation caused by the carrier frequency random hopping, the complex range-azimuth coupling effects will significantly deteriorate the target integration performance. In this letter, echoes are classified into different bursts according to the carrier frequencies, and then keystone transform (KT) is applied to correct range walk in every burst. After compensating the range offsets among different bursts and rearranging the signal returns, the scaled transform is constructed to remove the residual coupling between the agile carrier frequency and slow-time. Finally, a moving target can be well-focused in the frequency-velocity domain. Simulated results are provided to validate the effectiveness of the proposed algorithm.

In this letter, a novel method is proposed to estimate the along-track baseline for a multichannel high-resolution and wide-swath (HRSW) synthetic aperture radar (SAR) system. First, a spatial correlation function is constructed to remove the range cell migration and Doppler broadening of ground static targets. Then, the iterative adaptive approach (IAA) is applied to iteratively estimate the along-track baseline with high precision. Finally, a high-resolution SAR image can be obtained based on the estimated baseline. The effectiveness of the proposed algorithm is validated by both simulated and real SAR data.

The development of phased array radar beamforming technologies has led to an ever-increasing demand for large antenna arrays and multiple beams. However, real-time processing becomes a difficulty for large array or multibeam beamforming due to huge data amount. To overcome the electronic mass data processing speed limitations, optronic technique is developed for beamforming in this letter. We present a novel real-time multibeam optronic beamforming (OPBF) system specially designed for large phased array. In our system design, beamforming is formulated as a finite-impulse response filtering process, of which radar raw data and weighting coefficients are encoded onto the laser beam by joint amplitude and phase control (JAPC) modules and two-dimension adding is performed by a lens. Ultrafast optical calculation and the proposed high-speed JAPC module make the system powerful for real-time processing. The proposed system has good expansibility in data capacity, which makes it applicable to massive data processing. What’s more, a practical low-power optronic beamformer is demonstrated. Measured results show that the presented OPBF system is able to accurately synthesize flexible and controllable multibeams and performs well in channel equalization.

In this letter, a novel nonlinear supervised dictionary learning (DL) scheme called multiscale incremental DL, whose objective function contains reconstruction error terms and a classification error term, is proposed for synthetic aperture radar (SAR) object recognition. In the reconstruction error terms, considering the local and global features of SAR images, Gaussian functions with different blurring parameters are exploited to extract SAR images' multiscale features, and all features can be reconstructed according to the weights assigned to these features at different scales. In the classification error term, a linear combination of classification vectors close to the labels of samples restricts sparse codes from different classes to be almost independent. Furthermore, an incremental method is utilized to address the memory consumption problem, and the optimal solution is obtained. Experiments on the moving and stationary target automatic recognition database demonstrate that the proposed algorithm outperforms several representative DL, support vector machine, and k-nearest neighbor methods in the case of a small training sample set size and exhibits strong antinoise performance.

In this paper, a new algorithm is presented to image ground moving targets in a synthetic aperture radar (SAR) system based on range frequency reversal transform-fractional Fourier transform (RFRT-FrFT). In this algorithm, a range compressed signal is initially transformed into the range frequency domain and then RFRT is proposed to directly compensate the range migration via multiplying the signal in the range frequency domain by its reversed data according to the equal interval sampling of range frequency variable, which can significantly decrease the computational complexity in target envelope migration elimination. Then, FrFT is applied to accomplish the target motion parameter estimation after range migration alignment. Finally, a ground moving target is well focused after motion compensation. The effectiveness of the proposed algorithm is validated by both simulated and real SAR data.

Synthetic aperture radar (SAR) is easily affected by motion error during data acquisition. Motion error is very complicated and its range-dependent characteristic is studied in this paper. Compared with the original two-step motion compensation (MOCO), the proposed algorithm can compensate the residual range cell migration of scatterers away from the scene center. To realize it, the fast time scaling operation is performed. After it, we apply chirp scaling algorithm and form the image. Finally, the simulation experiment validates the proposed algorithm.

The improvement of synthetic aperture radar (SAR) resolution brings a broader applications but poses a great burden for SAR data processor. Real-time processing becomes a difficulty. Optronic technology has been developed for SAR realtime processing due to its ultrafast processing speed. A novel real-time optronic high-resolution SAR processor is proposed in this paper. It has the capability of full-resolution imaging. Restricted with the data scale of spatial light modulators (SLMs), SAR raw data cannot be all encoded onto the light beams at a time. To solve this, subaperture architecture is introduced in our system scheme. SAR data is optically processed in parallel by multiple optical subaperture processing modules and synthesized into a full-resolution SAR image. The module is implemented by multiple SLMs and lenses, which is innovatively proposed in this paper. The proposed system is applicable to large-scale SAR data processing, and the data scale is easy to extend with implementation of adding identical optical subaperture processing modules. Airborne SAR real data is used in the experiment, and a high-resolution image is also given. The PSLR of the focus results with and without subaperture partition are analyzed, which validates the satisfying image quality of the proposed algorithm.

In this paper, a new method is proposed to refocus a ground moving target in synthetic aperture radar imagery. In this method, range migration is compensated in the 2-D frequency domain, which can easily be implemented by using the complex multiplications, the fast Fourier transform (FFT), and the inverse FFT operations. Then, the received target signal in a range gate is characterized as a quadratic frequency-modulated (QFM) signal. Finally, a novel parameter estimation method, i.e., scaled generalized high-order ambiguity function (HAF), is proposed to transform the target signal into a signal on 2-D time-frequency plane and realize the 2-D coherent integration, where the peak position accurately determines the second- and third-order parameters of a QFM signal. Compared with our previously proposed generalized Hough-HAF method, the proposed method can obtain a better target focusing performance, since it can eliminate the incoherent operations in both range and azimuth directions. In addition, the proposed method is computationally efficient, since it is free of searching in the whole target focusing procedure. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

Scattering mediums in atmosphere always leads to the decrease of imaging quality and performance in remote sensing, especially in Geological Surveying and Mapping, and also in Military Reconnaissance. So it's necessary to propose some methods to improve imaging quality. Here our primary results show that it's possible to measure medium's scattering property with adaptive algorithm through focusing, and both simulation and experiment results are given to validate the adaptive algorithm.

Due to the restriction of the number of receivers, it is hard to realize high-resolution wide-swath (HRWS) imaging and moving target detection simultaneously for multichannel synthetic aperture radar (SAR) systems. This paper presents a novel moving target detection method in HRWS mode. A digital beamforming (DBF) technique in azimuth time domain is proposed to convert the ambiguous multichannel SAR signals into unambiguous single channel SAR signals. Azimuth ambiguities of both stationary targets and moving targets can be effectively suppressed. Finally, moving targets can be successfully detected by traditional moving target detection methods for single channel SAR systems without adding receivers and changing SAR operating mode. Theoretical analysis and experiments showed the feasibility of the proposed method.

This paper proposed a new concept of optical counterpart of radar system. Due to the difference between wavelength of microwave and light, we can build an optical counterpart of a radar system in reduced dimensions with optical components and light in place of microwave. The radar system is equivalent to its optical counterpart in physical principle. As for a C-band radar system, the wavelength is 5cm typically and 5 orders longer than 500nm green light. A scene of 10km for the C-band radar will be scaled to 0.1m for its optical counterpart. The optical counterpart of a radar system can simulate the backscattered signal in light-wave domain at a speed of light. Taking the advantage of reversibility of optical path, the optical counterpart can also be used to process the radar data and reconstruct the scene or target in spite of complex algorithms as long as the physical equivalence is kept well.

Digital beamforming (DBF) is wildly used in phased array radars, there are many algorithms and technologies to realize it. The traditional DBF architecture is based on digital signal processor. However, with the increasing requirements on target detection and resolution, the number of channel is getting more, the volumne of processing data is becoming larger, the capability of the digital signal processor is challenged. An optical system is proposed in this paper. The system is based on Digital Micromirror Deviece (DMD), it is designed to perform beamforming on receive for phased array radar. In this system, all calculation is realized in optical domain. That is, with the laser beam propogation, the calculation of beamforming is done. The system can form multiple beams simultaneously, the parameters of each beam can be controlled. The exprimental setup is built, and the beamforming results are given to validate the system performance.

Remote sensing is defined as the technology through which the characteristics of object on, above or even below the earths surface are identified, measured and analyzed without direct contact existing between the sensors and the targets. And nowadays, visible remote sensing plays an important role in providing accurate, large-scale information for many kinds of system. But visible remote sensing is easily influenced by the atmosphere. They are the scattering and absorption by the larger particles such as smoke, haze and fumes in the atmosphere, for which compensation is needed when correcting imagery. Here, we propose a method which could overcome these weakness and help visible remote sensing through the atmosphere. In this article, we are trying to take advantage of Transmission Matrix(TM) in optics to compensate for the effects of complex medium which is similar to the atmospheric particles in visible remote sensing. The principle of this way is that the TM can be exploited for focusing and image detection using well-established operators which used for inverse problems. We present the simulation and experiment results which validate the feasibility and effectiveness of the proposed method.

The Doppler centroid is a crucial parameter for the purpose of implementing azimuth processing for synthetic aperture radar (SAR) data. In this paper, we propose a scheme for estimating the Doppler centroid of conventional SAR. Virtual multi-channel SAR data can be generated by sub-sampling the original SAR data in azimuth. Consequently, each channel data is aliased and the spectrum components within each Doppler bin can be viewed as sources from different known directions. Then the Capon spectral estimator can be utilized to estimate the antenna pattern. Based on the estimated antenna pattern, we can obtain the baseband Doppler centroid of the original SAR data. Finally, experiments are provided to validate the effectiveness of the proposed algorithm.

In this paper, the problem of joint transmit waveform and receive filter design for cognitive radar (CR) is investigated. The problem is analyzed in signal-dependent interference, as well as additive channel noise for extended target with unknown target impulse response (TIR). An improved online waveform optimization design method is employed for target detection by maximizing the average signal to interference plus noise ratio (SINR) of the received echo on the premise of ensuring the TIR estimation precision. In the proposed method, the transmit waveform and receive filter are optimally determined at each step based on the observations in the previous steps. Simulation results demonstrate that CR with the proposed waveform achieve significantly higher rate of estimation accuracy and detection performance improvement compared to traditional radar system with fixed waveform, and offers more flexibility.

In this paper, the problem of adaptive waveform design for cognitive radar (CR) in signal-dependent interference, as well as additive channel noise is investigated. With constraints on waveform energy and bandwidth, a flexible waveform optimization method taking both detection and range resolution into account is proposed. Unlike existing optimal waveforms designed by a single design criterion, waveform designed by the proposed method can be updated according to the environment information fed back by receiver and radar performance demands simultaneously at each cycle of CR. Simulations are conducted to illustrate that CR with the proposed waveform performs better than traditional radar system with fixed waveform, and offers more practical and flexible.

With more degrees of freedom in the along-track axis, multichannel SAR systems are widely investigated for the purpose of ground moving target indication and motion parameter estimation. However, since conventional multichannel SAR system usually works at the side-looking mode and only the radial velocity is considered, it fails to detect targets only moving in azimuth. In this paper, the variable-boresight configuration is designed for multichannel SAR and overlapping imaging mode is applied to detect targets moving in azimuth. Besides, this system can also provide the precise velocity information of the moving target. Finally, the effectiveness of the proposed approach is verified with simulated multichannel SAR data.

This paper presents a new scheme for moving target velocity estimation in conventional high-resolution wide-swath (HRWS) SAR systems. A generalized steering vector is designed for azimuth ambiguity suppression and the velocity parameter of the moving target is involved in this modified steering vector. The low PRF in HRWS-SAR systems will result in serious azimuth ambiguity. When the adopted parameter is matched with the real velocity of the moving target, its azimuth ambiguities will be suppressed clearly. Entropy can be used to measure the performance for azimuth ambiguity suppression. The velocity can be estimated through searching the matched velocity. Finally, the effectiveness of the proposed approach is verified by simulated multichannel SAR data.

Multiple azimuth channels are usually employed to overcome the inherent limitation between high resolution and wide swath in synthetic aperture radar systems. However, unavoidable channel phase errors will significantly degrade the performance of ambiguity suppression. Conventional calibration methods usually regard these phase errors as constants during the whole observation time and ignore the azimuth-variant phase errors, which may not totally suppress azimuth ambiguities especially for very strong targets. This letter presents an azimuth-variant phase error calibration technique. The proposed technique first selects a strong point-like target as the calibration source. Then, the azimuth-variant phase errors can be estimated by comparing the phase of the calibration source and the corresponding steering vector in the range-compressed signals. Besides, a preprocessing method is presented to improve the calibration accuracy when the selected calibration source is affected by noise or interferences. Theoretical analysis and experiments demonstrate the feasibility of the proposed technique.

Multichannel synthetic aperture radar (SAR) signal reconstruction methods can effectively suppress azimuth ambiguities and achieve high-resolution wide-swath imaging. However, due to the characteristics of the practical antenna patterns, there exist non-bandlimited Doppler spectra, which will result in residual azimuth ambiguities, especially for strong targets. This letter presents a novel method for the suppression of the azimuth ambiguities of the strong point-like targets. First, we find out the positions of the strong point-like targets from the multichannel reconstructed SAR image. Then, we locate the ambiguous range history of each strong point-like target. Finally, the ambiguous components in the range history are filtered out by an orthogonal projection method. Therefore, the spectra of the strong point-like targets will be converted into the bandlimited spectra, and then, the azimuth ambiguities can be effectively suppressed by the conventional multichannel SAR signal reconstruction methods. Theoretical analysis and experiments demonstrate the feasibility of the proposed methods.

High-resolution and wide-swath synthetic aperture radar (SAR) imaging can be achieved by the azimuth multichannel system. The minimum variance distortionless response (MVDR) beamformer can be utilized to suppress azimuth ambiguities. However, the presence of channel phase errors significantly deteriorates the performance of the azimuth multichannel SAR system. Instead of employing subspace techniques, this letter proposes a robust channel phase error calibration algorithm via maximizing the MVDR beamformer output power. Compared with the conventional subspace-based calibration methods, there is no redundancy of channels required to estimate the subspaces in the proposed algorithm. Also, the proposed algorithm is relatively robust, because it avoids the subspace swap phenomenon, which probably takes place at low signal-to-noise ratios for the subspace techniques. Moreover, the proposed method has the advantage of estimating the channel phase errors without covariance matrix decomposition, which reduces the computation load. The simulation experiments and the real data processing validate the effectiveness of the proposed calibration method.

This letter presents a new scheme for ground moving target indication in high-resolution wide-swath (HRWS)-synthetic aperture radar (SAR) systems. The asymmetry of the Doppler spectra is measured to extract the range bins with moving targets. To improve the computational efficiency, only the extracted range bins are used to restore the unambiguous Doppler spectra. The two-look processing technique is then applied to generate two looks and moving targets are indicated by comparing the difference between the two looks. In this detection scheme, the configuration of the conventional HRWS-SAR system remains unchanged and no additional receiving channels are needed. The experimental results show the effectiveness of this detection scheme.

Matrix completion aims to find the missing entries from incomplete observations using the low-rank property. Conventional convex optimization based techniques for matrix completion minimize the nuclear norm subject to a constraint on the Frobenius norm of the residual. However, they are not robust to outliers and have a high computational complexity. Different from the existing schemes based on solving a minimization problem, we formulate matrix completion as a feasibility problem. An alternating projection algorithm (APA) is devised to find a feasible point in the intersection of the low-rank constraint set and fidelity constraint set. To achieve resistance to outliers, the fidelity constraint set is modeled as an ℓp-ball, where the ball center corresponds to the observed data. Furthermore, there is no stepsize within the framework of APA. Convergence of the APA is analyzed and the local linear convergence rate is established. Simulation results demonstrate the efficiency, accuracy, and outlier robustness of the APA.

Linear frequency modulated (LFM) continuous wave (CW) synthetic aperture radar (SAR) is promising in airborne earth observation with compact, lightweight, cost-effective and high resolution advantages. For CW imaging radars, however, weak targets may be submerged by sidelobes of strong range interferences including transmitter leakage and nadir signal, especially when the slant range is far. This paper firstly discusses how to suppress transmitter leakage and nadir signal suppression for LFMCW SAR by choosing appropriate pulse repetition interval (PRI) and digital sampling frequency. Although the transmitter leakage could be weakened by an appropriate PRI and finite impulse response (FIR) filter, the residual leakage is so strong that its sidelobes will still submerge some weak targets. Then, a novel transmitter leakage canceling method based on orthogonal projection is derived, which could effectively reduce the sidelobe level of transmitter leakage. Theoretical analysis and experiments on a real unmanned LFMCW SAR system showed the efficiency of the proposed method.

A novel method of three-dimensional (3D) imaging based on orthogonal-track synthetic aperture radar (SAR), is proposed in this paper. In the scheme, the SAR sensor moves along two orthogonal tracks successively and two SAR images are obtained. Then we extract the spacial information from the two-dimensional (2D) SAR images to reconstruct the 3D distribution of scatterers in the common part. The orthogonal-track SAR obtains resolving ability in the normal direction of the azimuth-range plane by combining the information obtained along orthogonal tracks. Compared with conventional single-channel 2D SAR and interferometry SAR, the orthogonal-track SAR has distinct advantage in providing detailed and precise information about spatial distribution of the observed scene, and is capable of achieving real 3D resolution cell.

Synthenic Aperture Radar (SAR) has an important role in the field of remote sensing. As optical SAR data processor has the outstanding advantage of high speed, it performs processing prospect in real-time SAR imaging field. Liquid crystal based spatial light modulators (SLMs) are widely used to make phase modulation in optical SAR processor. However, in some phase-sensing or real-time applications such as SAR imaging process, low phase precision of SLM may cause phase error and low data refresh rate of SLM may restrict processing speed. This paper proposes a new optical SAR data processor with high-speed and high-precision phase modulation. The core of this optical processor is a phase modulation module by using a digital micromirror device (DMD) which can achieve high-speed and high-precision phase modulation because of its high data refresh rate of 9500Hz and phase resolution of 0.002 rad. The principle of phase modulation with DMD and the structure of the new SAR optical processor are presented. The images processed by it are also presented.

Here automatic range cell migration correction (RCMC) is studied. Classic RCMC in Range-Doppler Algorithm (RDA) is realized by sinc interpolation so that exact instantaneous slant range distance between antenna phase center (APC) and scatterers is necessary. However, the later element above could not be satisfied especially in airborne SAR. Inspired by improved global method used for ISAR range alignment, we propose an automatic RCMC technique with Broyden-Fletcher-Goldfard-Shano(BFGS). This technique creates N variables (time shifts) to adjust range migration curve to make cost function minimum. Exact instantaneous slant range distance is not required while computation efficiency is a little lower than that of classic RCMC. Simulation validates this idea from the view of three isolate scatterers. It also proves that BFGS-RCMC has phase-keep property in azimuth direction.

An X-band Synthetic Aperture Radar (SAR), the mini-SAR, mounted on an eight-rotor Unmanned Aerial Vehicle (UAV), has been designed, built and tested at Shanghai Jiao Tong University, China. The main purpose of this work is to design a light-weight, cost-effective and easy-handy miniaturize SAR system with the ability to make repeated flights for an extended study. Real-time collected data can effectively test the validity of a newly proposed image algorithm. The system can apply in modeling and calculating the scattering characteristics of complex target such as tank which is vital in military reconnaissance. Recent tests have shown that the system is suitable for further experiments to validate the SAR system design via changing the parameter setting. This paper outlines design parameters and specifications for the mini-SAR, together with results from experimental data collection and test flights.

Along a roughly chronological order, the azimuth signals undersampled from the multichannel synthetic aperture radar (SAR) for high-resolution wide-swath (HRWS) imaging correspond to recurrent nonuniform sampling. Only a finite-duration sequence of samples of the azimuth signal can be obtained in practical applications. A new recurrent nonuniform sampling scheme can be generated by extending these samples periodically across the boundaries provided that the azimuth signal is bandlimited. Thus, from the perspective of reconstructing recurrent nonuniform sampling, an innovative reconstruction algorithm for suppressing azimuth ambiguities of multichannel HRWS SAR is proposed, which is suitable to be implemented on digital computers. Furthermore, the presented algorithm acquires the filter weights without matrix inversion reducing tremendously the computational load.

In this paper, a new scheme is presented for ground moving target indication for multichannel synthetic aperture radar (SAR) systems using the asymmetric distribution of the spatial spectrum. Two feature images are generated via accumulating energy intensities of the positive and the negative spatial spectrum, respectively. In the two images, the stationary backgrounds have the same intensity while moving targets are not due to their across-track velocities. Coherent subtraction is carried out to implement clutter suppression and potential moving targets can be indicated through the constant-false-alarm-rate (CFAR) test. When dealing with slow weak moving targets, this developed approach can still obtain good detection performance. Finally, the effectiveness of the proposed approach is verified by real airborne SAR data.

A method to solve multi-target classification problems with unknown clutter parameters is proposed in this paper. The unknown parameter is estimated and synthesized at each observation, and probability of each hypothesis is updated. Subsequently, the optimal waveform for the next illumination is designed based on NP criteria, and the final decision is made based on the sequential probability ratio testing. Simulated results are presented based on our method and show that the optimal waveform-based sequential testing can be decided through reduction of the average illumination number. Furthermore, results indicate a significant improvement over the non-optimal waveforms.

Radar performance improvement through waveform optimization has been an ongoing topic of research recent years. In this paper, we use the optimal waveform design method to deal with the moving target in the clutter and noise. Neyman-Pearson detector criterion is used to maximize the probability of target detection. The optimal waveform is then designed theoretically corresponding to the velocity of target and clutter/noise power spectrum density. Simple CW signals can produce maximum detectability based on different noise PSD situations. Simulated results are presented based on our method and improvement in image is approached. Finally, the conclusions are drawn based on our analysis and simulations.

A system design of optronic high-resolution synthetic aperture radar (SAR) processing is proposed in this letter; it has the capability of adaptive phase error compensation. In our system design, SAR raw data are focused optically by phase correction in the two-dimensional frequency domain. The computations of SAR image formation are performed by spatial light modulators and lenses. With the propagation of the laser beam, the imaging results can be captured by the charge-coupled device. The phase error can be estimated from the imaging results, and adaptive phase error compensation is implemented in the optronic processing. Phase error compensation makes the optronic processing robust and flexible; it can be used for both the initial system calibration and the focus quality improvement. Finally, the experimental setup is demonstrated. The entropies of the images with and without phase error compensation are analyzed, which validates its performance on improving the focus quality. The real data results of an airborne SAR and RADARSAT are given.

Multichannel synthetic aperture radar systems in azimuth can effectively suppress azimuth ambiguity and are promising in high-resolution wide-swath imaging. However, unavoidable channel errors will significantly degrade the performance of ambiguity suppression. Conventional subspace calibration methods usually estimate phase error via decomposing a Doppler-variant covariance matrix from one Doppler bin, and then average these errors estimated from several Doppler bins to improve the estimation accuracy, which will result in a large computational load. This letter presents an improved channel error calibration method, which works on the undersampled data of the individual azimuth channel. By a proposed matrix transformation method, the Doppler-variant covariance matrices will be transformed into a constant covariance matrix. Therefore, the improved calibration algorithm needs to estimate and decompose the new covariance matrix only once. The computation load could be greatly reduced. Moreover, the new covariance matrix can be estimated by training samples not only from range bins but also from Doppler bins, which will improve the estimation accuracy. Theoretical analysis and experiments based on simulations and measurements showed the high accuracy, efficiency, and robustness of the improved method, particularly in low signal-to-noise ratio.

The recently reported Wirtinger flow (WF) algorithm has been demonstrated as a promising method for solving the problem of phase retrieval by applying a gradient descent scheme. An empirical choice of stepsize is suggested in practice. However, this heuristic stepsize selection rule is not optimal. In order to accelerate the convergence rate, we propose an improved WF with optimal stepsize. It is revealed that this optimal stepsize is the solution of a univariate cubic equation with real-valued coefficients. Finding its roots is computationally simple because a closed-form expression exists. Furthermore, compared with obtaining the coefficients of the cubic equation, calculating the gradient is still the leading cost. Therefore, the proposed approach has the same dominant cost as WF in each iteration. Simulation results are provided to validate its efficiency compared to the existing technique.

Synthetic Aperture Radar now is widely used in remote imaging which have many kinds of system. But as SAR data become larger and larger, the traditional Digital Signal Processing processor can not afford the high speed and high resolution process in rated size and power. But our optical processor for SAR can finish the data process in high speed because it can perform the Fourier transformation at the speed of light with low power consumption. Besides, this system is automatic and programmable which can be realized by computer and spatial light modulator.

In order that global, dominant, and complete outlines of land covers are delineated, in this paper, we propose a regularized L0gradient minimization method which is specially developed for segmenting polarimetric synthetic aperture radar (PolSAR) images, and present a region-based stepwise design to implement it. The performance of the proposed method is tested and analyzed on two experimental data sets, with visual presentation as well as numerical evaluation. They both confirm that the proposed method achieves its principal goal and demonstrate its availability and advantage as a pre-processing step for PolSAR image interpretation chains.

The fractional Fourier transform (FRFT) is a potent tool to analyze the chirp signal. Here a novel SAR imaging technique based on FRFT is studied. We apply FRFT to range compressed data instead of executing RCMC and Azimuth compression. By FRFT, we estimate (1) Doppler rate, (2) amplitude (3) azimuth beam center crossing time which means azimuth location of a point target in image in accurate way. The relative range locations of point targets can be also determined by the formulation of Doppler rate. Then SAR image can be reconstructed together with estimated amplitudes. The relationship between FRFT order and SAR resolution is also studied. Furthermore, we simulate this method from a multi-points view.

Microwave staring imaging scheme based on range pulse compression and azimuth wavefront modulation is a new radar scheme. Under the condition of no relative motion between radar and scene, the scheme can obtain radar images with high resolution in both range direction and azimuth direction. The paper presents this imaging technique by theoretical analysis and simulation. Microwave staring imaging structure is introduced firstly. Then, requirement of the azimuth wavefront modulation and target change detection is analyzed.

In this paper, we combine the waveform design method with the synthetic aperture algorithm to suppress clutter and generate clear microwave images of targets. The linear recursive model is introduced into the SAR operation principle and Kalman filter algorithm is used to estimate target and clutter responses in each azimuth direction based on their states before, which both are assumed to be Gaussian distributions. Optimal waveforms based on NP criteria are designed repeatedly and used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. The simulations show that our algorithm can significantly reduce the clutter response while targets are imaged.

It is believed that it is essential to take the spatial adaptivity into the segmentation method for polarimetric synthetic aperture radar (PolSAR) images. The size and shape of each segment and the strength of the relationship of neighboring pixels need to depend on the local spatial complexity of the scene. The wedgelet framework provides a promising analysis tool for spatial information. The major advantage of the wedgelet analysis is that it captures the geometrical structure of images at multiple scales, with the local spatial complexity taken into consideration. Hence, in this paper, we propose a wedgelet approximation and analysis framework specially designed for PolSAR data. Based on this framework, a spatially adaptive representation and segmentation method is constructed and presented. It mainly consists of three parts: first, the multiscale wedgelet decomposition is applied to the PolSAR image, and the local geometrical information is captured in an optimal way; then, the image is segmented in a spatially adaptive manner by the multiscale wedgelet representation in the form of the regularized optimization, which keeps a balance between the approximation and parsimony of the representation; the final part is the spatial-complexity-adaptive segmentation refinement based on the Wishart Markov random field model. The performance of the proposed method is presented and analyzed on two experimental data sets, with visual presentation and numerical evaluation. It is also compared with an existing and theoretically well-founded segmentation method. The experiments and results demonstrate the availability and advantage of the proposed method.

Optronic processing is a promising way to real-time highresolution SAR image formation. Rang migration is inevitable in SAR imaging, especially in the case of highresolution SAR system, range cell migration correction (RCMC) is considered in nature. A solution of RCMC by using optronic processing is presented in this paper. In this optronic solution, most computation is undertaken by optical devices, while the data control is undertaken by electronic devices. The architecture of the experimental setup is presented, and the experimental results are given.

An automatic DPCA technique is presented for SAR Ground Moving Target Indication (GMTI). We note that there exists a shift and a phase difference between the images from two channels. Therefore, SAR-GMTI can be implemented in the following steps: Image registration, phase compensation, image subtraction, and CFAR detection. In our technique, these steps are carried out automatically, and thus no precise information is needed about the length of the baseline and the velocity of the platform. We utilize a set of real data to demonstrate the accuracy and the robustness of our algorithm.

In this paper, a multi-temporal (MT) superpixel generation method is proposed. MT superpixels are generated based on a novel edge extraction method designed for high resolution (HR) synthetic aperture radar (SAR) images and using MT spatial information in an optimization way. Numerical evaluation and comparison on simulated and real data sets demonstrate its availability for MT HR SAR image analysis.

Synthetic Aperture Radar (SAR) is a powerful tool for alltime and all-weather imaging. As the technology developed, the resolution of SAR image is required much higher, and the processing time is required shorter. Traditional DSP processor is difficult to process the high resolution complex SAR data of 3 meter and 1 meter, or even high. The optical processor performs the most promising capability, since the Fourier lens provide inherent parallel computing. In addition, it can perform the Fourier transformation at the speed of light. This paper proposes a tiling project to perform the full resolution SAR data imaging.

Synthetic aperture radar (SAR) is an active microwave sensor which is able to produce high-resolution images with day and night capability. Traditionally, the image quality assessment of the SAR system is described by the response function of an isolated point target. However, some man-made targets which are placed together can not be distinguished clearly. As the point target has the characteristic of the isotropic scattering, the retrieval capability for target structure details and contours by using the point target assessment method cannot be assessed accurately in a practical scene. In this paper, a new image quality assessment method (especially for plane targets) is proposed to solve this problem. The definitions of the plane grid and the plane grid resolution are given to support. After giving the scene construction of the Test Field used for measuring the plane grid resolution (PGR), a corresponding algorithm is also proposed. Experiment results show that the proposed assessment method describes the SAR image quality for man-made targets more accurate than the traditional assessment method in a practical scene.

An innovative algorithm to reconstruct complex target-induced azimuth envelope in synthetic aperture radar (SAR) system is proposed. Unlike the assumption in conventional SAR imaging algorithms, target's backscattering coefficient can hardly remain constant when synthetic aperture time is long enough. In order to fully understand the target feature, we extract both amplitude and phase information of target-induced azimuth envelope from SAR raw data. In this paper, we formulate range migration curve (RMC) with a parametric model and implement curve fitting to estimate these parameters. The input pixels of curve fitting process is extracted from range compression result of raw data. Applying the idea of random sample consensus (RANSAC), this algorithm classifies pixels according to the target's RMC they belongs to, and extracts target's feature information at the same time. Experimental results are conducted to validate this algorithm.

Microwave staring imaging scheme based on range pulse compression and azimuth wavefront modulation is a new radar scheme. Under the condition of no relative motion between radar and scene, the scheme can obtain radar images with high resolution in both range direction and azimuth direction. The paper presents this imaging technique by theoretical analysis and simulation. Microwave staring imaging scheme is introduced firstly. Then, imaging properties and target change detection is analyzed.

In this paper, we proposed a clutter suppression algorithm for SAR imaging due to different target power spectrum density (PSD) and clutter PSD in azimuth direction. The optimal waveform of the SAR system is designed according to the prior-knowledge of the target and clutter, an amplitude limiter set in frequency domain is used to suppress the clutter response and 2-dimentional pulse compression is then used to the SAR imaging. Simulated results are presented based on our method which is shown great improvement in target image over clutter image. Then conclusions are drawn based on our analysis and simulations.

The classic region-based filter for edge extraction for polarimetric synthetic aperture radar images is theoretically founded and efficient. However, in practical use, its performance is limited because the assumption of independence and identical distribution is often not met, particularly in heterogeneous areas. In this letter, we present a degenerate filter design integrated with the weighted maximum likelihood estimation to overcome this limitation. The performance of the proposed methodology is presented and analyzed on both simulated and real experimental data sets using visual presentation, as well as numerical evaluation and comparison with the classic method. They both demonstrate the availability and advantage of the proposed method.

Ground moving target indication (GMTI) with synthetic aperture radar (SAR) is a very hot research topic in recent years. The traditional methods are based on multi-antennae technology, such as displaced-phase-center-antenna [1], along-track interferometry [2], and space time adaptive processing [3], and many space-borne or air-borne radars have been developed and put in use. However, the multi-antennae based technology is very complex and costs so much. Many researchers try to perform the tasks by using a single-antenna SAR. For example, J. R. Fienup detects the moving target by using autofocus technology based on shear averaging method [4], J. Dias et al. use the antenna radiation pattern information to indicate the moving targets and estimate their velocities [5], and G. Lv et al. detects and estimates the azimuth velocity component based on the symmetric defocusing method [6]. They can give fairly effective results. However, the mentioned methods exploit the phase difference between the background and moving targets, and the effectiveness will be influenced by clutters and interference. A new GMTI scheme used to detect moving targets with range velocity components is proposed in this paper based on background cognition. The scheme consists of four parts: a normalized background Doppler spectrum set (BDSS), a correlator used to extract the background of a given azimuth vector in a SAR imagery, a normalized moving targets' Doppler spectrum set (MTDSS), and a classifier used to optimally classify the moving targets according to the waveform design algorithms [7]. The output of the classifier are feed back to the correlator to estimate the background further. As a result, the classifier gives more accurate estimations of moving targets. It is a closed-loop procedure. The scheme works well in our recent experiments.

Synthetic aperture radar (SAR) is an active microwave sensor which is able to produce high-resolution images of the earth surface, with all-weather day and night capability. Conventionally, SAR image quality assessment is typically done by evaluating the signal of the corner reflectors which are positioned in the scenery, and a specialist is needed to extract some characteristic parameters (e.g. the 3-dB impulse response width, the PSLR and the ISLR) to assess the SAR image quality. But it is still an unsolved problem that whether the SAR image quality assessment based on current parameters can predict the reliable probabilities of target recognition from the high-resolution SAR images. For EO sensors (Electro Optical sensors), the image assessment is assessed by the evaluation of MTF (Modulation Transfer Function), with non-specialists. In this paper, a new concept for SAR image quality assessment is proposed to solve this problem. The definitions of spatial frequency and MTF are given to support the new concept. After giving the scene construction of a TEST Field used for measuring MTF, an MTF measuring algorithm is proposed. Experiment results show that the proposed assessment method can describe SAR image quality more accurately and effectively than traditional PSF assessment method.

In this paper, we present an image processing chain that can interpret high resolution synthetic aperture radar (SAR) imagery for building layover characterization and extraction in urban areas. It is composed of three main parts - generation of hint areas, generation of superpixels, and optimized cut of layovers via superpixel merging. The proposed framework is complete, and flexibly integrates necessary information, both area and boundary, for building layover extraction; the experimental results show that its performance is promising.

This paper uses ray tracing technique to simulate a complete imaging process of Synthetic Aperture Radar (SAR). The simulator can collect the raw data and generate accurate shadows of the object. The radiation pattern is defined by a spotlight to have more realistic imaging effect. Considering the high performance computing of modern graphics processing units (GPU), we transfer the ray tracing algorithm from CPU to GPU. Thus, the simulator can work on a large 3D scene with relatively high efficiency.

In this paper we analyze the problem of waveform design for target detection in the presence of clutter and noise environment. We use the target priori characteristics to design the transmitted waveform. The waveform is designed by mutual information criterion. Firstly, we present the signal model and information theoretic approach to design waveform. Secondly, we discuss the waveform design is the one that maximizing mutual information between the target priori characteristics and the radar received signal. The results of simulation evaluate the efficiency of the proposed method to compare with the chirp signal.

Here feature extraction of a generic SAR target is studied. We apply a new data model to target feature extraction and improve SAR image quality. The data model we construct contains (1) envelope, (2) location of the target. The envelope shape can be controlled by four parameters, and location information is indicated by frequency pair in both range and cross-range. These parameters would be estimated by nonlinear least squares (NLS) and 1-D Cramer-Rao Bounds (CRB) for these parameters have also been analyzed. Numerical examples show this method can achieve CRB at high SNR and its computational complexity is acceptable.

This paper presents a novel and compressive SAR raw data simulator based on CUDA via GPU. The stripmap synthetic aperture radar(SAR) is introduced to model antenna illumination behavior. Compared with conventional raw data simulators, we no longer limit our research interests on a single point target's raw data simulation, but expend it to that of complex scene. In order to compensate the greatly increasing operational time with booming computational complexity, we optimize the process in two aspects. In the first, modern GPUs have the potential for highly parallel calculation, and it makes them much more efficient than CPUs in processing large blocks of data. Therefore, we implement the simulator on CUDA. The second method is to take advantage of symmetry in single raw data matrix based on stripmap mode SAR, and reduce 75 percent computational complexity. By these two effective methods, the simulator experiences an attractive operating time and enjoys high efficiency. Some simulation results prove the simulated raw data is acceptable in accuracy.

A new algorithm is presented to indicate the ground targets moving in azimuth in synthetic aperture radar (SAR) images. This algorithm is based on local two-Look similarity. In the two looks of the scene, a stationary target has the same positions, but a moving target has different positions. Therefore, moving targets can be indicated by the patch-by-patch similarity detection between the two looks. We use normalized correlation to characterize similarity. In the similarity detection, for each patch in the first look, only the normalized correlations with three patches in the second look are calculated, and thus this algorithm is computationally efficient.

Onboard SAR data imaging is increasingly important for many satellite applications. And optical SAR processor is one promising solution due to its extremely quick calculation speed, simple structure and strong radiation hardness. Recent relative researches done by researchers of National Optics Institute in Canada prove the feasibility of optical processor, but its resolution of 30 meter is relatively low. This paper proposes a novel high resolution optical SAR processor which is based on the Range Doppler Algorithm. The processor includes two main elements: the optical one-dimensional Fourier transformation element and the spatial light modulator (SLM). The usage of several SLMs gives wider parameter adjustment boundary and more flexibility to the processor.

In this paper, we apply the optimum waveform design method to meet the application of elastic targets detection and aim at enhance the target detection rate. The target is assumed to be Gaussian elastic targets and the clutter is assumed to be a stationary Gaussian random process. The detection problem then is described using Neyman-Pearson Detector. The waveform design solutions for elastic targets are given theoretically, and numerical simulated results are presented based on different transmit energy and comparison results between optimum signal and LFM signal are shown. Finally, the conclusions are drawn based on our analysis and simulations.

A new algorithm is presented to indicate ground moving targets in synthetic aperture radar images. Two filters, which differ only in the signs of the phase responses, are used to defocus the complex image respectively. In the two defocused images, each stationary target is blurred to the same extent, but each moving target is blurred to different extents. Therefore, moving targets can be indicated by the patch-by-patch sharpness comparison of the two defocused images. The results of the simulated and real data show that this algorithm is effective and efficient.

Polarimetric synthetic aperture radar (PolSAR) image classification, an important technique in the remote sensing area, has been deeply studied for a couple of decades. In order to develop a robust automatic or semiautomatic classification system for PolSAR images, two important problems should be addressed: 1) incorporation of spatial relations between pixels; 2) estimation of the number of classes in the image. Therefore, in this paper, we present a novel superpixel-based classification framework with an adaptive number of classes for PolSAR images. The approach is mainly composed of three operations. First, the PolSAR image is partitioned into superpixels, which are local, coherent regions and preserve most of the characteristics necessary for image information extraction. Then, the number of classes and each class center within the data are estimated using the pairwise dissimilarity information between superpixels, followed by the final classification operation. The proposed framework takes the spatial relations between pixels into consideration and makes good use of the inherent statistical characteristics and contour information of PolSAR data. The framework is capable of improving the classification accuracy, making the results more understandable and easier for further analyses, and providing robust performance under various numbers of classes. The performance of the proposed classification framework on one synthetic and three real data sets is presented and analyzed; and the experimental results show that the framework provides a promising solution for unsupervised classification of PolSAR images.

It is necessary to measure the sharpness of distributions in many situations. A class of functions is investigated in this paper. First, the relation between this class and sharpness is clarified, and this justifies this class as sharpness measures. Then, we analyze the performance of different sharpness measures and present a guide to select the sharpness measure. In addition, the relation of this class to the sparsity measure is addressed, which leads to a deeper understanding about sparsity. Finally, we show and discuss the application of this class in inverse synthetic aperture radar imaging.

A novel land-cover classification framework for HR SAR images which combines low-level features and category context is presented in this paper. We use patch-based features for low-level information extraction, including average intensity, texture within a patch and the super texture we proposed to model the texture similarity of neighboring patches. To represent the local category context of SAR images, we propose the label layout filter. This work resolves local ambiguities of low-level features from a category context perspective. The framework demonstrates good performance in both accuracy and visual appearance for HR SAR scene interpretation.

Conventional SAR imaging algorithms form SAR image based on imaging geometries, the resulting image indicates the spatial location of illuminated targets with respect to radar platform. Scattering characteristics of targets are overlooked by these algorithms. We construct a model of received SAR signal in terms of (1) envelope, (2) phase, (3) duration, (4) arrival time, (5) frequency center and (6) chirp rate, and the envelope shape can be controlled by two parameters. Arrival time, frequency center and chirp rate indicate the spatial location and motion dynamics of the targets, while information of scattering characteristics can be analyzed from the other parameters. The parameters of the model are then estimated via atomic decomposition. In this paper, the impact of target-induced envelope on data focusing is analyzed, furthermore, target feature can be extracted from a signal-level point of view.

In this paper, we improve the traditional bag-of-words-based image representation method in two aspects: preserving the semantics in vocabulary generation and incorporating spatial relations in image representation. Based on that, we present a novel context-aware information modeling method for high resolution synthetic aperture radar image scene interpretation. We compare the proposed method with traditional ones in scene interpretation on TerraSAR-X data sets.

In this paper, an unsupervised change detection method based on space contextual information and EM algorithm is proposed. In the algorithm, each pixel of the difference image is represented by a characteristic quantity constructed from the difference image values considering the space contextual information. EM algorithm is used to achieve the parameter estimation of each class pixels. Bayesian inference is then employed to perform the final change detection results. Experimental results obtained on multi-temporal optical images acquired by Landsat 5 TM confirm the effectiveness of the proposed approach.

In this paper, a theory of two-dimensional transmit-receiver design is presented. Only deterministic targets and random clutters and noises are considered in this model, and we reach two-dimensional optimization based on maximization signal-to-interference-and-noise ratio(SINR). New result focuses on extending the previous one-dimensional waveform design to two-dimensional one, in order to satisfy some practical application like synthetic aperture radar(SAR). Both the theoretic derivation and simulation result proves that 2D radar imaging can gain high SINR with proper transmit-receiver design.

It takes complex coding work to develop a remote sensing applications which needs to display various remote sensing data. To solve this problem, we propose a remote sensing information display platform of web-browser pattern. A 3D remote sensing browser is designed to parse kinds of information and display them in a 3D scene. The remote sensing information is organized as a page file according to a format we define in this paper. Finally, through a use case, we illustrate the convenience of developing remote sensing application on our platform.

In this paper, we propose a coarse-to-fine framework design and implementation for oil tank detection in optical satellite imagery. The framework is mainly composed of two operations: 1) from the whole scene imagery, extraction of patches with oil tanks based on the probabilistic latent semantic analysis model; 2) in the relatively small size patches, detection of the oil tanks with Hough transform and template matching. Experiments show that the framework provides a promising solution for oil tank detection in optical satellite imagery.

Radar high-resolution range profiles (HRRPs) are very sensitive to amplitude-scale and time-shift, and their good recognition performance depends on precisely the matching methods of both sensitivities, thereby, the method to handle these sensitivities is a challenge in the field of Radar automatic targets recognition(RATR). However, most approaches available did not pay much attention to this problem. This paper proposes two algorithms to jointly matching the amplitude and time-shift for training and test phase, respectively, taking Automatic Gaussian Classifier (AGC) as an example. Experiments based on measured data show our algorithms have a remarkable larger average recognition rate than that of the regular method for AGC.

Automatic detection and recognition of ship in satellite images is very important and has a wide array of applications. This paper concentrates on optical satellite sensor, which provides an important approach for ship monitoring. Graph-based fore/background segmentation scheme is used to extract ship candidant from optical satellite image chip after the detection step, from course to fine. Shadows on the ship are extracted in a CFAR scheme. Because all the parameters in the graph-based algorithms and CFAR are adaptively determined by the algorithms, no parameter tuning problem exists in our method. Experiments based on measured optical satellite images shows our method achieved good balance between computation speed and ship extraction accuracy.

This work is devoted to the proposal of the algorithm management framework for remote sensing image analysis. A hierarchical framework of algorithm management is first introduced. Then we establish an algorithm description model and corresponding reasoning mechanism based on the quotient space problem solving theory and graph theory. With this model the developers can avoid wasteful duplicate algorithm development and complicated parameter adjustment during the algorithm development process for remote sensing image. The effect and efficiency of the framework has been proved through our preliminary experiment results.

The conventional SAR imaging algorithms are developed based on specified imaging geometry models, and these algorithms become more complex considering finer resolution or more complicated geometry. The quality of the product is measured by resolution, PSLR and ISLR, etc. without regard to its application. This paper proposes a signal-model-based imaging scheme. Independent of imaging geometry, the new scheme focuses the backscattered echoes by estimating the signal parameters. Furthermore, two realizations of the scheme are presented. This scheme will also have great potential for target feature extraction and image interpretation.

In this paper, we propose a CS-based SAR imaging algorithm after analyzing the model of echoes from point target. Our reconstruction algorithm is two-dimensional, unlike current one-dimensional CS-based SAR imaging algorithms. It can reconstruct targets with high resolution from relatively small number of echoes and effectively improves the efficiency of reconstruction. The processing results of simulated data demonstrate the effectiveness of our algorithm.

In this paper, we present a number-of-classes-adaptive unsupervised classification framework for synthetic aperture radar (SAR) images. The framework aims at the provision of robust classification for SAR images even if the number of classes existing in the scene is unknown. It mainly consists of estimation of the number of classes, extraction of each class center, classification of image patches, and integration of spatial relations between patches. The experiment on a TerraSAR-X SAR image shows that the proposed framework presents a promising performance for SAR image classification.

In this paper, we present a scene interpretation framework for Synthetic Aperture Radar (SAR) images, using keywords of the image contents provided by users. The framework consists of incorporation of prior knowledge with SAR iMage Annotation Tool (SARMAT), representation of SAR images, and prediction of scene labels based on the supervised Latent Dirichlet Allocation (sLDA) model. The experiment on a TerraSAR-X SAR image shows that the proposed framework provides a promising performance for SAR image scene interpretation.

A virtual array concept is proposed to get supper-resolution in synthetic aperture radar imagery. Two kinds of virtual arrays called virtual frequency array and virtual azimuth array are constructed, combining with phased array signal processing methods, to bring out better imaging performance than traditional range-Doppler and reciprocal spectrum algorithms used in previous literatures.

A new scheme is presented for the velocity estimation of moving targets in synthetic aperture radar (SAR) imaging. First, the moving target is imaged using a range-Doppler algorithm, where clutter lock, range correction and focus filtering are automatically done. Then, the parameters obtained in this algorithm are used to estimate the velocity of the moving target. The Doppler centroid is used to estimate the velocity of the moving target in range. The out of-focus coefficient is used to estimate the velocity of the moving target in azimuth. Especially, the ambiguity of the Doppler centroid is resolved according to the range correction coefficient and the out-of-focus coefficient. This scheme is accurate and computationally efficient.

In this letter, we present a novel foreground/background separation (FBS) framework for interpreting polarimetric synthetic aperture radar (PolSAR) images. The FBS framework takes the spatial relations between pixels into consideration and incorporates the advantages of pairwise dissimilarity-based grouping schemes. The FBS method can separate specific targets and objects from the background, which is essential in an interpretation system. Multiple FBS operations can be integrated to interpret PolSAR images, flexibly fusing various inherent features of PolSAR data. Several PolSAR data sets are used to verify the proposed approach.

A radar imaging algorithm named reciprocal spectrum algorithm (RSA) is proposed in this paper to get higher resolution in azimuth direction with frequency sampling waveform. Theoretical analysis and simulation results show that the algorithm can give better performance than the tradition range Doppler algorithm (RDA) at the cost of peak value reduction at the object points in radar image.

A progressive SAR imaging technique is proposed as a novel SAR raw data processing scheme in this paper. Different from the classic SAR imaging algorithms which focus the SAR raw data as a whole regardless of the backscattering signal, the novel scheme discriminate the backscattering signal and choose the proper ones to be focused progressively according to some application context while the others will be discarded. The new scheme makes the SAR imaging algorithm not only focus the energy back to the scattering points but also form an image more suitable for some special applications. In this paper, a realization of the scheme present via Atomic Decomposition (AD)[1]–[3]. AD helps estimate the parameters of backscattered signal of a scattering point and detach it from the raw data. With the parameters, the backscattering-signal can be reconstructed and focused. Targets or scatter-points in the scene will be imaged progressively in a sequence of their energy due to the greedy natural of matching pursuit employed during AD. Therefore, the contents in the final SAR image can be controlled by an energy threshold. Besides the interested targets can be extracted easily from the background clutter and this may be helpful for the SAR image understanding

A new technique is developed for the automatic correction of range migration in synthetic aperture radar imaging. In the range-Doppler domain, samples at a Doppler frequency constitute a Doppler slice. It is noted that, when there is no range migration, the Doppler slices have similar envelopes, i.e., the envelope of one Doppler slice roughly equals the envelope of another Doppler slice multiplied by a constant. Thus, range migration can be corrected by shifting the Doppler slices such that their envelopes are similar. The technique applies even when the radar moves irregularly or the target is moving.

A novel approach is proposed in this paper to exploit the shuttle radar topography mission (SRTM) digital elevation model (DEM) in the interferometric synthetic aperture radar interferometry (InSAR) processing. The proposed algorithm includes three steps: the first step is to patch the void cells in the SRTM DEM; the second step is to determine a one-to-one correspondence between the interferogram and the SRTM DEM; the third step is to eliminate the phase trend between the original and simulated interferogram. Meanwhile this algorithm can be applied to help the phase unwrapping problem. Conventional techniques approach phase unwrapping as an optimization problem, where the total branch-cuts, or the gradient errors, etc. are to be minimized. Generally speaking, they consider phase unwrapping as a blind procedure, i.e., without any external guidance. The purpose of this paper is to fill this gap by introducing the SRTM DEM as a phase unwrapping guidance. Some experimental results with JESR verify our theoretical analysis and show that our method can improve the performance of the phase unwrapping to a great degree.

Dual-antenna single-pass synthetic aperture radar interferometry needs alignment of both antenna beams to achieve the best interferometric performance. Meanwhile, geosynchronous synthetic aperture radar, potentially used for global earthquake prediction and many other attractive applications, also requires antenna pointing control system to steer the radar antenna to illuminate desired territory, otherwise, even slight deviation from ideally boresight direction can cause a great variation of footprint position, because of the large slant range from the radar to mapped area. In principle, it is possible to measure antenna pointing information directly; however, measurement uncertainties will limit the accuracy. Thus it is feasible to resort to received radar data to measure the antenna pointing. This paper concentrates on the antenna pointing measurement using onboard Doppler centroid estimator, and furthermore, to drive pitch and yaw angles to steer the antenna pointing. In order to realize real-time onboard processing, a novel Doppler centroid estimation algorithm, called sign-MLCC, is presented here, utilizing the phase information of the received signal and the arcsine law by analyzing the sign alone, then evaluation of the algorithm is discussed. Finally, simulations are shown to prove the validity and reliability of the proposed method.

A novel GPU based time-domain raw signal simulator for InSAR is proposed in this paper to exploit the parallel computation of GPU using CUDA language. This simulator combines the advantages of both time-domain and frequency-domain InSAR simulator, i.e., it considers the baseline oscillation and real orbit, and it is also very efficient. Experimental results show the effectiveness of the simulator in varieties of conditions.

An effective method is proposed to remove the flat-Earth phase (i.e., flattening) in airborne single-pass interferometric synthetic aperture radar (InSAR) imaging. Two conventional flattening methods, namely, the orbit-equation algorithm and the fringe-frequency algorithm, are used for comparison. The orbit-equation algorithm is theoretically accurate, but the orbit ephemeris that it requires is maybe inaccurate or even unknown. The fringe-frequency algorithm is effective in spaceborne InSAR flattening, but in airborne cases, a phase trend will remain in the residual interferogram with this method. To overcome these limitations, this letter presents a novel flattening algorithm by combining the two conventional methods for airborne single-pass InSAR flattening. By exploiting a reasonable model of the flat-Earth phase and the spectrum information of the practical interferogram, the proposed algorithm is able to flatten the interferogram accurately without knowledge of the airplane trajectory (except for the airplane height). Finally, some experimental results demonstrate the effectiveness of the proposed flattening algorithm.

For an azimuth-invariant bistatic synthetic aperture radar (BiSAR), not only is the secondary range compression dependent on range, but also the range-cell migration in the range-Doppler domain is nonlinearly dependent on range. To get a better focusing performance, the two range-dependent factors must be taken into account in an imaging algorithm. In this letter, a nonlinear chirp-scaling algorithm is extended for focusing the BiSAR data under a large baseline and a wide range swath. This algorithm leads to more effective processing with computational efficiency, and no interpolation is required.

This paper presents a new approach to reconstruct digital elevation model (DEM) without compensating the flat earth phase back to the unwrapped interferometry in the Interferometric Synthetic Aperture Radar (InSAR). The new approach is based on an accurate flattening algorithm called model-spectrum algorithm which combines the advantages of classic algorithms. The experimental results show that the new algorithm has a better performance than the conventional ones. Based on this novel algorithm, DEM reconstruction can be implemented by a quasi-linear scaling after phase unwrapping. There is no need to add the flat earth phase back to the flattened interferogram, which avoids complex geometrical conversion as what is done in the conventional algorithms.

This paper presents an improved step transform subaperture approach for spotlight Synthetic Aperture Radar (SAR). The step transform algorithm will obtain desired high azimuth resolution only if the linear FM rate and the sampling rate of the SAR signal satisfy a certain constraint. In order to obtain a high quality image, the minimum entropy autofocus using an adaptive order polynomial model is extended and applied before the coherent summation step in this paper. The extended autofocus processing not only compensates quadratic and higher order phase errors but also shifts the coarse-resolution response functions to their correct positions. The simulation results of 1-dimension and 2-dimension are given, and the validity of this algorithm is demonstrated.

A new technique is presented for range-migration correction in SAR imaging. In the range-Doppler domain, the samples at a Doppler frequency constitute a Doppler slice. Different Doppler slices have similar envelopes. Based on this similarity, the Doppler slices are shifted and aligned in range to correct range migration. The technique applies even if the prior knowledge about the relative motion between the radar and the target is unavailable.

The time-frequency method is a promising phase adjustment technique for inverse synthetic aperture radar (ISAR). Usually, the time-frequency method estimates the translational Doppler frequency and thus the translational Doppler phase by detecting the peaks of the time-frequency representation at different times. Unfortunately, it has some defects, such as the sensitivity to noise and target scintillation, and the aliasing of the time-frequency representation due to undersampling. In order to remove these limitations, we develop an improved time-frequency method. It estimates the translational Doppler frequency from the envelope correlation of the instantaneous slices of the time-frequency representation. Compared with the traditional time-frequency method, this technique can estimate the translational Doppler frequency more accurately. Moreover, we develop a preprocessing method to avoid the aliasing of the time-frequency representation due to undersampling.

In this letter, an algorithm based on a quartic-phase model is discussed for processing highly squinted synthetic aperture radar (SAR) data from a large range swath. In the algorithm, a precise quartic-phase model is adopted to describe a range-dependent property of the SAR signal; a constant factor and a secondary scaling process are introduced to make the algorithm easy to be utilized compared with traditional nonlinear chirp scaling algorithms. The novel algorithm can process SAR data under a squint angle above 50deg and achieve a focus depth over 60 km

In this paper some improvements of Non-linear Chirp Scaling (NCS) approach are proposed and a modified algorithm based on the NCS is described. The improvements include three main aspects. A quartic polynomial model is adopted to represent SAR signal in Range-Doppler domain and 2-D frequency domain to improve precision. A constant scaling factor is employed to replace the reference frequency in the NCS, which makes NCS more flexible. A second chirp scaling operation is added to the end of the NCS so as to remove scaling effect on range direction of SAR image. Those improvements make NCS applicable to process highly squinted SAR data from a large range swath at a fine resolution.

Eldhuset's research, the fourth-order Extended Exact Transfer Function (EETF4), indicates that the range history of a high spatial resolution spaceborne SAR can be modeled by a fourth-order Taylor expansion in azimuth time. In this paper, this point of view is introduced in bistatic SAR, and the transfer functions in the 2-D frequency and the range-Doppler domains are derived. Using this method, an efficient bistatic focusing solution based on squinted mode chirp scaling (CS) algorithm which accommodates both tandem and TI case is developed. Quantitative analysis and simulations are also provided.

A new algorithm is presented for autofocus in synthetic aperture radar imaging. Entropy is used to measure the focus quality of the image, and better focus corresponds to smaller entropy. The phase response of the focus filter is modeled as a specially designed polynomial, and the coefficients of this polynomial are adjusted in sequence to minimize the entropy of the image. Because the order of this polynomial is adaptive, this algorithm applies more widely than the minimum-entropy algorithms with a fixed-order polynomial model

A new point of view is proposed to correct range migration and focus highly squinted SAR data. The procedure of range-migration correction is done not only in the range-Doppler (R-D) domain and the 2D frequency domain as usual, but also in the time domain. A modified chirp scaling (CS) algorithm is described, based on the new point. A quantitative analysis and simulations show that the new algorithm can process SAR data with a squint angle as large as 70/spl deg/ and a range swath of 50 km or wider.

The global range alignment has a good performance in inverse synthetic aperture radar (ISAR) imaging. The shifts made to the echoes are modeled as a polynomial, and the coefficients of this polynomial are chosen to optimize a quality measure of range alignment. The shift in the time domain is implemented by introducing a phase ramp in the frequency domain in order to remove the limitation of integer steps. However, in this method, the quality measure of range alignment need be calculated in the time domain, which is time consuming. In this paper, we present a new expression for the quality measure of range alignment so that it can be calculated directly in the frequency domain. This raises the computational efficiency of the algorithm significantly.

High-frequency skywave over-the-horizon radar (OTHR) can provide a wide coverage over the horizon by means of the refraction within the ionosphere. However due to the complex propagation conditions of electromagnetic waves in the HF band, many disadvantageous effects, such as phase path contamination and multimode propagation, cause the target to be submerged by the neighboring spread clutter. In this paper, the spread effect of identical clutter, backscattered from the adjacent dwell illumination region (DIR), has been discussed. On the basis of the existing matched field processing (MFP) method developed for this problem, the multiple-dimension cross-relation method, using the multiple channel signals, has been proposed and used for the temporal data directly. Experimental simulations are given to demonstrate that the improved method is effective in suppressing the spread clutter.

The randomly intermittent spectra (RIS) signal is a technique that is increasingly employed to combat spectrum congestion in radar and other radio services, to evade the external interferences. However, the spectra discontinuity of the signal gives rise to high range sidelobes when matching the reflected echo, which is much more difficult for target detection. So it is indispensable to investigate techniques for sidelobe suppression of the range profile when an RIS signal is utilized. In this paper, we introduce a new processing technique, based on time domain filtering, to lower the range sidelobes. A robust and effective algorithm is adopted to solve the coefficients of the filter, and the restriction on the desired response of the filter is derived. The simulation results show that the peak range sidelobe can be reduced to -27 dB from -9.5 dB while the frequency span band is 200 kHz.

Motion correction is required in airborne synthetic aperture radar to generate high quality images. The paper proposes a new motion compensation method based on subaperture techniques which better approximates the space variant of the compensation kernel. Motion errors are averaged and added to the system parameters. Only residual errors remain to be corrected. The subaperture images are compensated independently and added together coherently in the final step to generate a fine resolution image. Simulation results show that the new method outperforms full aperture compensation methods, especially in the case of large motion errors.

A new idea is presented to correct range migration in SAR imaging. In the range-Doppler domain, all the samples at a given Doppler frequency constitute a Doppler slice. Different Doppler slices are found to have similar envelopes. According to this similarity, the Doppler slices are shifted in range to correct range migration. This technique applies even without the prior information about the relative motion between the radar and the target

A new algorithm for spotlight SAR imaging under squint mode is proposed in this paper. The structure of the algorithm is rather simple and can be divided into two parts: the sub-aperture imaging with range-walk removal and sub-band combination, which are relatively independent

This paper addresses a parametric method based on high-order ambiguity function (HAF) to solve the problem of phase contamination of HF skywave radar signals corrupted by the ionosphere. When signal-to-noise ratio and data sequence available satisfy the predefined conditions, the ionospheric phase contamination may be modeled by the polynomial phase signal. As a new parametric tool for analyzing polynomial phase signal, HAF is applied to estimate polynomial phase model parameters and reconstruct the disturbance signal. Using the estimated reconstructed signal, compensation can be performed before coherent integration and the original radar return spectrum can be restored. A piecewise scheme is proposed to track rapid variation of the phase contamination in HAF method, and it can remove the Doppler spread effect caused by the ionosphere nonstationarity. Simulation is used to demonstrate the efficiency of the proposed method.