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

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

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

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

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

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

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

Compared with the traditional tomographic synthetic aperture radar (TomoSAR) system, multistatic TomoSAR can overcome the physical size limitation, realizing high-resolution 3-D imaging via single pass. However, due to the minimum safety distance limitation between flight platforms, the maximum unambiguous imaging range of multistatic SAR is relatively small, which is difficult to meet the mapping needs of urban high-rise. To address this problem, this paper proposes a multistatic TomoSAR ambiguity suppression method based on multiple subbands. This method can effectively achieve ambiguity suppression and 3-D reconstruction of the target without grating lobes. First, a multistatic TomoSAR imaging model is established. Second, we analyze the mechanism of multiple subbands ambiguity suppression. Finally, we utilize the adaptivity of the sparsity Bayesian recovery via iterative minimum (SBRIM) algorithm to the number of targets to realize multistatic TomoSAR 3-D imaging. The effectiveness of the proposed method is validated by simulations.

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

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

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

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

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

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

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

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

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

In recent years, many countries are actively deploying Low-Earth-Orbit (LEO) communication satellite constellations, which have the advantages of both high power flux density (PFD) on the surface of the earth and large signal bandwidth. From the perspective of radar application, these new LEO constellations are very suitable as opportunity of illuminator for target detection in passive radar systems. In this paper, the echo signal using LEO communication satellite is analyzed, and a moving target detection method is proposed.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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