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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.

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 non-stationary and non-homogeneous 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 BiSARs 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 non-stationary 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 multi-measured 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 non-stationary 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 non-stationary 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.

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.

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

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

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

Bistatic SAR (BiSAR) imaging is becoming more and more mature, nevertheless restricted by the significant problem of 2-D space-variation of echo signal. In practical applications, the geometry configurations and platform motion of BiSAR are often complicated, making the problem of space-variation severe and causing it hard to efficiently process BiSAR data of large imaging scene. Focusing on this problem, this article proposes a new imaging method for large scene-size imaging that can adapt to general spotlight BiSAR with complicated platform motion. This method first roughly focuses the echo data in range-Doppler domain through azimuth-dechirp and removes the residual range-azimuth coupling via Keystone transform. Then making use of the tolerance of SAR imaging to slight phase error, the full Doppler axis is down-sampled with a certain ratio to form a much smaller amount of Doppler cells called Doppler bands. Through restricting the value of the down-sampling ratio, the variance of differential phase in each Doppler band after the dechirp is controlled within a given tolerance. As a result, the differential phase can be compensated for in variable Doppler bands for a large imaging scene with high efficiency. The computational complexity of the proposed method is indicated as low as logarithmic and its effectiveness is verified through numerical simulations.

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

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 $L_{1}$ regularization 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.

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

Bistatic synthetic aperture radar (BiSAR) imaging is faced with two major challenges: large scene imaging and adaptability to unideal platform motion in practice. In order to deal with these two problems, a generalized wavefront-curvature-corrected polar format algorithm (PFA) is proposed in this article. The traditional PFA is little restricted on geometry configuration and platform motion, but its application to large scene imaging is limited by the far-field planar wavefront assumption. To solve this limitation, this article derives the phase error caused by wavefront curvature and analyzes its influence on both geometric distortion and defocusing effect in detail. Based on the analysis, we present a wavefront curvature completely correcting method through space-variant phase compensation using the analytical wavefront curvature phase in wavenumber-domain, which is derived through method of series reversion. What's more, an efficient realization of the space-variant phase compensation based on two-stage image division is given to avoid high overlap rate in the traditional image division method. The proposed method can obtain well focused and geometric undistorted image for BiSAR under complicated flight paths, and it also keeps the logarithmic complexity of traditional PFA. The effectiveness of the proposed method is verified by numerical simulations.

In recent years, the research of multistatic synthetic aperture radar (SAR) has become a hot spot because of its great potentials in military and civil applications. However, it is inevitably disturbed by electronic countermeasures. In the multistatic SAR image, the false target caused by transmitting deceptive jamming leads to misjudgment of the image information. In this article, a new antideceptive jamming method for multistatic SAR is proposed. This method can locate the deceptive jammer and effectively suppress it. First of all, the echo model of multistatic SAR in the jamming environment is set up. Based on this model, the relationship expression for the location of jammer is derived. Then, we use the maximally stable extremal region method and the Euclidean-distance-based method to detect and identify false target in the multistatic SAR image. By using the location information of false target, receivers, and transmitters, the expression of the jammer localization is solved to locate the jammer. Finally, the minimum variance distortionless response beamforming method and image mosaic method are used to effectively suppress the jamming. The simulation results show that the method is effective.

Synthetic aperture radar (SAR) has extensive applications in both civilian and military fields for its ability to create high-resolution images of the ground target without being affected by weather conditions and daytime or nighttime effect. As fueled by the several decades’ advancement of SAR, existing SAR systems exhibit high imaging capabilities, namely, significantly high two-dimensional resolution. However, in accordance with Nyquist's sampling theory, the increase in resolution implies an evident increase in the amount of sampling data, thereby causing numerous limitations to practical application. To solve this problem, several compressive sensing (CS) and matrix sensing (MS) techniques have been applied to SAR imaging, wherein the existing knowledge of sparsity or low-rankness is exploited to reconstruct the SAR image based on an under-sampled SAR raw data. In this study, we take a different approach, wherein redundancy property of the SAR image is further exploited. The SAR image is split into a sparse matrix and a low rank matrix. Thus, the SAR imaging processor is modelled as a problem of joint sparse and low rank matrices recovery. An Orthogonal Sparse and Rank-one Pursuit (OSRanP) algorithm is newly proposed to solve this problem in SAR imaging case, where there isn’t any prior information of the exact sparsity or low-rankness value at hand. As revealed from the results here, the proposed method outmatches the CS and MS methods in sampling efficiency in both simulations and experiments.

In real application scenario of bistatic forward-looking synthetic aperture radar (BFSAR), ground moving target (GMT) is generally smeared severely in SAR image, due to its unknown range cell migration (RCM) and Doppler frequency migration (DFM). When GMT moves along an unknown curve trajectory, its high-order RCM and DFM (including the second- and third-order terms) would further aggravate the difficulty of GMT refocusing. To address this issue, an efficient GMT refocusing method via keystone-based delay-correlation transform and fast searching Fourier transform (KDCT-FSFT) is proposed. First, the KDCT is proposed to correct the first- to third-order RCMs regardless of target's motion state and position information. Meanwhile, the order of GMT's phase response is reduced as well. Then, FSFT is applied to estimate the third-order Doppler parameter of GMT. In the following, a 2-D fast Fourier transform (2D-FFT) can be applied to integrate the target signal coherently in Doppler parameters domain, where the Doppler centroid and Doppler frequency rate of GMT can be estimated. Finally, with the aforesaid estimated Doppler parameters, RCM and DFM can be well corrected and target with unknown curve motion can be finely refocused. Compared with the existing methods, not only the refocusing accuracy of the proposed method is higher, but also its processing is more efficient, since the procedures in the proposed method are performed with respect to all the range cells in the corresponding aperture, i.e., GMT refocusing is achieved by the 2-D data received in one aperture, rather than along every single range cell. Both the simulation and experimental results are given to validate the effectiveness of the proposed method.

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.

A bistatic forward-looking synthetic aperture radar (BFSAR) is increasingly in the focus of study, as it breaks through the limitations of imaging on a forward-looking terrain of a moving platform. Fast factorized backprojection (FFBP) is a reliable BFSAR imaging algorithm, with both imaging precision and efficiency being taken into consideration. In FFBP imaging, compensation of the motion errors is important to obtain a well-focused image. To accomplish an accurate motion compensation in image processing, a high-precision navigation system is needed. However, in many cases, because of the accuracy limit of such systems, motion errors are difficult to be compensated correctly, resulting chiefly in resolution decrease in the final images. To deal with such a problem, we propose an FFBP imaging algorithm integrated with motion trajectory estimation for BFSAR. First, a coarse-to-fine residual range cell migration (RCM) correction scheme is applied to ensure the residual RCM within a range resolution. Then, an optimization model with respect to motion trajectory estimation under the criterion of maximum image sharpness is built during FFBP imaging. According to the coarse-to-fine residual RCM correction scheme and the motion trajectory estimation model, a block coordinate descent technique based on steepest descent optimization method integrated with residual RCM correction is proposed to estimate the motion errors in FFBP imaging. We empirically compare the proposed method with several state-of-the-art BFSAR imaging and autofocus algorithms. Simulations on BFSAR data show that the proposed method is more accurate and has similar computational cost.

Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset.

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.

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.

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

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

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

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

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

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

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

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

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.

Polar format algorithm (PFA) as is an efficient algorithm can be well applied to spotlight bistatic synthetic aperture radar (SAR). However, a planar wavefront assumption which leads to image distortion is made. In this study, a new method to compensate for the wavefront curvature of PFA image is presented and it can well correct the defocusing effect and geometry distortion even under arbitrary platforms paths. By projecting the coefficients of Taylor expansion from the azimuth-time domain to wavenumber domain, the new method does not need repeated interpolations to construct the compensation filters for all the errors, which can decrease computational expense in a way. Finally, the effectiveness of this method is verified by numerical simulation.

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

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

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

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

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

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

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

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.

The vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) is promising to benefit the higher degree of freedom for synthetic aperture radar (SAR) imaging. Combining the traditional two-dimensional (2D) SAR and the vortex EM wave, vortex SAR can obtain the three-dimensional (3D) information to realize 3D imaging. This letter proposes a novel 3D vortex SAR imaging method based on the vortex EM wave to obtain 3D target information. First, a vortex SAR imaging model is established by the proposed 3D imaging algorithm. Then, the modified range-doppler (RD) algorithm is therefore proposed to reconstruct the target's profiles. The objective of the 3D RD algorithm is to decompose the time-domain correlation operations of the SAR 3D space into three independent one-dimensional correlations and then perform separate focusing processing on each dimension. Finally, the 3D information of the targets can be obtained according to the modified RD algorithm. The point target simulation is performed in this paper. The simulation results establish the effectiveness of the proposed technique. This paper lays the groundwork for the development of vortex EM waves in the field of 3D radar imaging.

Motion error and compensation is a crucial problem for bistatic SAR. In this paper, based on the geometry model of bistatic SAR considering motion error and topographic variation, the concept of bistatic SAR iso-MoCo curve is proposed, which refers to the contour line of motion error compensation for bistatic SAR. Analytical expression of bistatic SAR iso-MoCo curves under flat terrain conditions is deduced via a conic section model. Properties of motion errors considering spatial variation with and without topographic variation are analysed based on simulation results.

The imagery of airborne highly squinted synthetic aperture radar (SAR) with curved trajectory is a challenging task due to the translational-variant range cell migration (RCM) and azimuth modulation. However, in most cases of practical application, the curved trajectory cannot be accurately known, which brings greater difficulties to the imaging problem. To accommodate these issues, we propose a novel motion modelling and optimisation based imaging algorithm for the highly squinted SAR with unknown curved trajectory. First, to correct the translational-variant RCM, a coarse-to-fine RCM correction scheme as well as a range perturbation approach is applied. Afterwards, an optimisation model of motion information under the criterion of minimum entropy is built during the azimuth processing by nonlinear chirp scaling (NLCS). Correspondingly, a differential evolution (DE) optimisation strategy is proposed to estimate the motion information in an iterative manner. We empirically compare the proposed algorithms with several state-of-the-art highly squinted curved SAR imaging algorithms. Numerical results show the effectiveness of the proposed method in the case without any prior information of the curved trajectory.

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 this paper, an imaging method for GEO-LEO SAR is proposed. After analyzing the sampling characteristics of GEO-LEO SAR, it is found that only 12.5 % sampling data can be acquired in azimuth direction. To handle the serious sub-Nyquist sampling problem and achieve good focusing results, an imaging method combined with multi-receiving technique and compressed sensing is proposed. The multi-receiving imaging model is firstly obtained based on the inverse process of a nonlinear chirp scaling imaging method. Then, the imaging problem of GEO-LEO SAR is converted to an L 1 regularization problem. Finally, an effective recovery method named complex approximate message passing is applied to obtain the final nonambiguous image. The simulation results show that the proposed method can suppress 8 times Doppler ambiguity and obtain the well focused image with 3 receiving channels.

Radar high resolution imaging becomes a research hotspot in recent years. Synthetic Aperture Radar (SAR) uses motion of single platform to obtain large aperture. However, it can only obtain a limited resolution at a specific perspective. Distributed radar, which utilize platforms to expand in space and form a larger aperture. It records echo from multiple signal channels which can achieve high resolution in all directions. The existing distributed radar system mainly realizes the imaging of static target with lots of platforms. In this paper, we studied moving target imaging of distributed radar in sparse array condition. In this system, all the array elements can work flexibly as transmitter or receiver. Some methods of spectrum estimation are used to solve distributed radar imaging model. But they are all applied to static target and it is difficult to estimate the number of scattered points accurately, especially for moving target. We established the signal model of moving target in a period of observation time and solved it by using truncated singular value decomposition (TSVD) method. Simulation results verified the feasibility of the imaging model and excellent performance of proposed method.

Reliable automatic ship detection in Synthetic Aperture Radar (SAR) image plays an important role in the surveillance of maritime activity. But, the coastal background has seriously affected the accuracy and speed of ship detection. Within this paper, we present a Dual Downscaling Spectral Residual (DDSR) method of sea-land segmentation-based spectral residual for ship detection that attempts to combine the downscaling thresholding operator, saliency computation and morphological features. For this, sea-land segmentation based on the downscaling thresholding operator and morphological features of the potential detection result is applied, which is followed by downscaling Spectral Residual detector so that the accurate detection result is obtained. Measured data illustrate that the proposed method obtain outstanding performance in ship targets detection.

Synthetic aperture radar (SAR) owns a lot of advantages such as high-resolution imaging, day-and-night working and so on, which has been widely applied in military and civilian areas. In this paper, we propose a time-delay and Doppler range model (TDRM) for SAR, which achieves very high accuracy, and is easy to use because it is two-dimensional linear with respect to time-delay and Doppler frequency. First, by establishing a polar coordinate system, the range history of SAR in the new coordinate system is obtained. Then, by combining round-trip time-delay and Doppler frequency with TDRM, we derive the range model in time-delay and Doppler domain. Finally, the analytical expression of TDRM is obtained by first-order Taylor approximation. We simulate TDRM with a specific SAR system. The simulation results verify the validity of the model. Specifically, for all targets in $2km\times 2km$ scene, the relative errors and the quadratic phase errors of TDRM are less than $7\times 10^{-9}$ and $\pi/4$ , respectively.