Search

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

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

Bistatic 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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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

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