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

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.

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

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

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

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.

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.

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

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.

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

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.

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

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.

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

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

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

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

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.

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

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

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

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

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

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.

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

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.

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

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.

Multiview synthetic aperture radar (SAR) images could provide much richer information for automatic target recognition (ATR) than from a single-view image. It is desirable to find optimal SAR platform flight paths and acquire a sequence of SAR images from appropriate views, so that multiview SAR ATR can be carried out accurately and efficiently. In this paper, a novel optimization framework for multiview SAR ATR is proposed and implemented. The geometry of the multiview SAR ATR is modeled according to the recognition mission and flight environment. Then, the multiview SAR ATR is abstracted and transformed into a constrained multiobjective optimization problem with objective functions considering the tradeoffs between recognition performance and efficiency and security. A specific approach based on convolutional neural network ensemble and constrained nondominated sorting genetic algorithm II is employed to solve the multiobjective optimization, and optimal flight paths and corresponding imaging viewpoints are obtained. The SAR sensor can thus choose an applicable flight path to acquire the multiview SAR images from different tradeoff solutions according to application requirements. Finally, accurate recognition results can be obtained based on those multiview SAR images. Extensive experiments have shown the validity and superiority of the proposed optimization framework of multiview SAR ATR.

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

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

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

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

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

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

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

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

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

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

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