We consider using the maximum-likelihood (ML)-inspired methods, such as maximum-likelihood inspired adaptive robust iterative approach (MARIA), for tomographic synthetic aperture radar (TomoSAR) imaging in scenarios with high noise level. Traditional MARIA method faces two key challenges in this case: First, the positive feedback mechanism likely leads to error propagation in high noise level; Second, the assumption of exactly known noise power is usually invalid, both of which result in significant performance degradation. Therefore, we propose a refined ML-inspired TomoSAR imaging method suitable for high noise level, improving the signal and noise estimation process of the typical MARIA. First, we derive a new iterative expression based on ML criterion without the positive feedback mechanism for signal estimation, which suppresses the error propagation in high noise level. Second, we regard noise power as a variable to be estimated and introduce an iterative method based on ML criterion for estimation. Simulation and real unmanned aerial vehicle (UAV) experiment results both verify the effectiveness of the proposed method. Finally, we theoretically give a convergence guarantee for the proposed method.

Multistatic unmanned aerial vehicle synthetic aperture radar (MUAV-SAR) three-dimensional (3-D) imaging system suffers from the time and phase synchronization errors among multiple stations. The classical two-way direct wave pulse exchange synchronization method introduces the π-ambiguity phase error and causes limited time-phase synchronization accuracy with multiple system nodes, leading to 3-D images defocusing. An MUAV-SAR joint synchronization method based on multiple direct wave pulses exchange is proposed to solve the π-ambiguity problem robustly and improve the synchronization accuracy significantly. Firstly, the π-ambiguity phase error is estimated through the comparative calculation of delay-phase information extracted from direct wave pulses and the high estimated success probability (99.73%) of the π-ambiguity can be achieved through the noise smoothing. Secondly, the synchronization accuracy is improved, that is, the time and phase error are reduced to about √2/Nof the existing method by utilizing N stations information fusion to jointly process redundant information of direct wave pulses from multiple synchronization links. Finally, a four-station UAV SAR real data experiment verifies the effectiveness of the proposed approach.

For the traditional 1-D nadir altimetry measurements, the spatial resolution of the sea surface height (SSH) measurements is restricted. However, the new wide-swath altimeters like Surface Water and Ocean Topography (SWOT) are anticipated to enhance spatial resolution and thereby improve the accuracy of derived marine gravity anomalies, particularly in high-frequency signals. The study investigates the efficacy of L2 KaRIn Beta data of SWOT in recovering high-frequency marine gravity signals. CryoSat-2 data was used for comparison. The first-order difference of shipborne gravity data along ship tracks was used as the reference to investigate the high-frequency accuracy of gravity derived from SWOT and CryoSat-2 at different point spacings, respectively. The research results show that compared to CryoSat-2 satellite data, when the point spacing is less than 2 km or greater than 6 km, the accuracy improvement is relatively small, less than 3.0%. However, within the 2–4 km range, the accuracy improvement ranges from 6.8% to 7.9%. When the point spacing is between 4 and 6 km, the accuracy improvement ranges from 5.0% to 6.7%. Therefore, when the point spacing is greater than 2 km and less than 6 km, the accuracy improvement is obvious. The results show that SWOT wide-range altimeter satellite has obviously improved the accuracy of traditional altimeter gravity inversion in high frequency (especially in the range of 2–6 km).

Tomographic synthetic aperture radar (TomoSAR), as a 3-D imaging technique, is widely applied in urban mapping. In our previous work, a multi-master (MM) TomoSAR approach was proposed for the long-baseline observation configuration. This article focuses on the MM TomoSAR for a common observation configuration. The main contribution includes two aspects: theoretical complement and performance extension. For the theoretical complement, the theory of virtual difference coarray (VDCA) in array signal processing (ASP) is introduced to the MM TomoSAR. First, we prove that the MM TomoSAR processing is equivalent to constructing a VDCA in the elevation direction, which essentially explains its principle. Then, to solve the redundancy problem in MM TomoSAR, the equivalent VDCA in the MM model is constructed by selecting the elements from the covariance matrix of the observed signal. Finally, the improved MM TomoSAR processing method is proposed. For performance extension, it is demonstrated that the performance improvement of the MM model is extended from the original two aspects (sidelobe suppression and high estimation accuracy) to include the third aspect (aperture expansion, which improves elevation resolution). The computer simulation and experiment based on TerraSAR-X data verify the proposed method effectively.

The accuracy of the traditional compressed sensing (CS) based tomographic synthetic aperture radar (TomoSAR) imaging is limited by the inappropriate grid partitioning. The atomic norm based processing effectively solves this problem by implementing variable estimation in the continuous domain, that is, avoiding the undesired grid partitioning manipulation. Nevertheless, the performance of the atomic norm based TomoSAR imaging is limited in two main aspects: limited geometry adaptability caused by the uniform sampling requirement and the high computational load. In this paper, a novel atomic norm minimization (ANM) based off-grid TomoSAR imaging is proposed for the fast processing with nonuniform sampling. The main technical contributions are twofold: First, the nonuniformly sampled data is resampled to be uniform where a new geometrical projection-based interpolation is used; Second, the ANM problem is solved by using the non-symmetric cone model to speed up the processing, reducing the computational load fromO(N2) toO(N). The proposed approaches have been verified by the computer simulations and the real data experiments.

The swath of the conventional spaceborne synthetic aperture radar (SAR) is parallel-to-orbit, making it inefficient to observe long curved terrain, like coastlines, railways, etc.. Imaging long curved terrains with the terrain matching (TM) curved swath is a promising technique for efficient data acquisition. The key feature is the employment of a long curved swath matching with the orientations of the long curved terrains. This paper reports the first demonstration of the spaceborne SAR TM curved imaging with the LJ2-01 satellite. A TM curved swath of 161.7 km is imaged with an azimuth resolution of 0.6 m. The main technical contributions are: First, a new electrical-mechanical-combined beam control method is proposed to achieve uniform azimuth resolution; Second, a new non-uniform pulse repetition frequency sequence is used to mitigate the data loss caused by the violent spatial variation of the slant range; Third, a new swath-adaptive sub-aperture time-domain imaging algorithm is proposed for efficient TM curved swath imaging. These innovations contribute to successful data acquisition and imaging of the spaceborne SAR TM curved imaging with the LJ2-01 satellite.

Lightweight video compression has effectively alleviated the tension between growing transmission demands and expensive integration upgrades. Effective rate control algorithms are believed to be the crucial bottleneck for quality improvement during those ultra-high throughput coding processes. This paper proposes a novel rate control (RC) scheme that constructs a contextual adaptive bit estimation model through clustering historical compression information into block-gradient complexity categories. A buffer-aware tuning method and a flexible quantization parameter (QP) mapping algorithm are designed to determine the Luma/Chroma QP distribution where a simplified Lagrangian multiplier is further defined to preserve the stability of the overall compression process. As a result, the constant-bitrate compression towards low-latency interoperable ASICs is implemented with a promising RC performance.

Due to the advantage of flexible and rapid deployment, unmanned aerial vehicle (UAV)-based synthetic aperture radar (SAR) tomography (TomoSAR) is a promising technology in 3-D urban mapping. The long baseline is indispensable for P-band SAR systems to achieve high elevation resolution. It will introduce two problems. On the one hand, the unavoidable spatial decorrelation brings serious phase noise and sidelobes in 3-D imaging. On the other hand, the noticeable image distortion fails the image registration and the TomoSAR data stack (TDS) construction. Aiming at the above problems, this article proposes a multimaster (MM) TomoSAR approach via three main contributions. First, the traditional TomoSAR signal model is extended to the MM case to improve the number of baselines and the average image coherence of the TDS and suppress the sidelobes. Second, a short-baseline-recursion image registration method is proposed to achieve high-precision image registration. Third, a TDS optimization processing consisting of interferometric SAR (InSAR) phase screening and baseline sign reassignment is introduced. Moreover, a clustering-based outliers’ elimination method is also adopted to ensure the 3-D imaging quality. Computer simulation and long-baseline P-band UAV-SAR experiment validate the proposed approach.

The compressive sensing (CS)-based tomographic SAR (TomoSAR) 3-D imaging method has the shortcoming of low efficiency, mainly represented in two aspects: first, the CS solver requires iterative calculation and hence is computationally expensive; second, the CS solver needs hyperparameters’ selection, which commonly requires cost-inefficient try-and-error attempts. Recently, the iterative CS solver is suggested to be replaced by a deep learning network for a tremendous processing speed improvement. However, the existing deep-learning-based TomoSAR imaging algorithms suffer from the problem of model inadaptability, i.e., being inadaptive to the observation model and the signal energy model and hence is low accuracy. This article proposes a new model-adaptive network (MAda-Net) to implement deep-learning-based TomoSAR 3-D imaging with a much improved processing accuracy. First, a new adaptive model-solving (AMS) module is introduced to solve the problem of the observation model inconsistency between the real spatially varying one and the approximately fixed one used by the network. Second, a new adaptive threshold-activation (ATC) module is introduced to solve the problem of signal energy model inconsistency between the real backscattered echo and the simulated echo for network training. The effectiveness of the proposed method has been verified by the computer simulations and the real unmanned aerial vehicle (UAV) SAR experiments.

As an important development of traditional SAR 2-D imaging, Synthetic aperture radar (SAR) 3- D imaging's core is sparse signal processing. However, due to the nonlinear characteristics of sparse signal processing, it often needs iterative calculation, which makes it inefficient. Researchers have put forward some ideas of using deep learning neural networks to quickly solve nonlinear signal processing problems, but it is lack of comparative analysis of different network performances. Therefore, this paper analyzes the abilities of two deep learning neural networks (ISTA-Net and ADMM-Net) to solve the 3-D imaging problem of tomographic SAR. Their quantitative performance in imaging accuracy and imaging efficiency is emphatically discussed, which can provide theoretical reference for subsequent deep learning SAR 3-D imaging research. The effectiveness of the analysis is verified by the measured data of UAV SAR.

Dual-channel synthetic aperture radar (SAR) is a widely used technology to achieve high-resolution and wide-swath imaging in current SAR system. Due to the fact of nonuniform sampling and spectrum ambiguity, the Doppler spectrum must be reconstructed before focusing. However, the traditional spectrum reconstruction algorithms require large onboard storage memory and computational resources and hence, make it hard to realize real-time processing. To solve the problem, this letter proposes a blocked spectrum reconstruction algorithm for onboard real-time imaging. Different from the traditional ones, the new method updates parameters block by block in azimuth, and hence, reducing the storage and computational resources at the cost of sacrificing the accuracy of the spectrum reconstruction to some extent. Fortunately, the accuracy loss is acceptable under properly set focusing depth, i.e., azimuth block width. The simulation and real data experiments verify the proposed approach.

Tomographic synthetic aperture radar (TomoSAR) is a 3-D imaging technology used to overcome the layover problem faced by traditional 2-D SAR systems. The quality of TomoSAR imaging capabilities relates closely to the number of acquisitions (NOAs). However, this number is often quite limited due to cost problems. The previous studies have shown some empirical requirements for the minimum NOAs. In this letter, an analytic constraint between the minimum NOAs and signal-to-noise ratio (SNR) is presented, and this constraint is more precise than the existing empirical one. The signal model is first analyzed, followed by the solution of the Fisher matrix, and finally leads to the constraint between the SNR and the minimum NOAs. The presented approach is evaluated via computer simulations.

Unmanned-aerial-vehicle-based (UAV-based) ultrawideband and ultrawidebeam (UWB) synthetic aperture radar (SAR) is very sensitive to atmospheric turbulence and suffers from serious 2-D space-variant motion errors (SVMEs) caused by the ultrawide beam and frequency-dependent phase errors caused by the ultrawideband. This article proposes an autofocus approach for UAV-based UWB SAR data based on the quasi-polar grid fast factorized backprojection (FFBP) imaging framework, multiple subband local autofocus (MSBLA), and trajectory deviation estimation. First, based on an improved weighted phase gradient autofocus (WPGA) method for subband-division local images, MSBLA is introduced to solve the local motion error estimation problem with frequency-dependent phase errors. Then, trajectory deviation estimation based on the weighted least square (WLS) method is performed to solve the 2-D SVME problem. Finally, the subaperture trajectory deviations are fused into a full-aperture trajectory deviation by an improved fusion strategy based on piecewise weighting. This approach is applied to real data from a new UAV-based UWB SAR. The results of both simulation and real data experiments are presented and verify the effectiveness of the proposed approach.

Multifrequency (MF) interferometric synthetic aperture radar (InSAR) phase unwrapping (PU) technology is proposed for PU in steep terrains where the phase changes of adjacent pixels exceed the commonly required threshold of$\pi $. Traditional MF PU methods will fail in the case of extremely steep terrain such as artificial buildings due to insufficient quality of phase noise suppression (PNS). In this article, we propose a refined MF-InSAR PU method that can be robustly applied for extremely steep terrain PU via two main contributions. First, an additional steep edge extraction step is introduced for geological local PU window generation to prevent inaccurate PNS across the extracted steep edges. Second, the traditional linear phase model is extended to a nonlinear one for more accurate MF local fringe frequency estimation in PNS. The computer simulations and the real dual-frequency airborne experiment validate the presented approach.

Linear array (LA) multiple-input–multiple-output (MIMO) synthetic aperture radar (SAR) has the capacity of achieving 3-D images in a single pass. If processed by matched filtering-based linear 3-D imaging, sidelobe suppression is often required for image quality enhancement. However, in the case of imaging a large target in a short range, sidelobes of the target will become spatially variant and curved, and traditional sidelobe suppression methods will fail. This article proposes a new spatially variant curved sidelobe suppression method for LA MIMO SAR short-range 3-D imaging. The key technique is the employment of a new pseudopolar coordinate system where both the spatial variance and the curvature of 3-D sidelobes can be removed. Specifically, a new 3-D spatially variant apodization (SVA) kernel is applied for sidelobe suppression to maintain the resolution performance. The validity of the presented approach has been demonstrated via computer simulations, the tower crane experiment, and the unmanned ground vehicle experiment.

Due to the extremely high orbital height and long synthetic aperture time, the geosynchronous synthetic aperture radar (GEO SAR) will inevitably suffer from different types of undesired errors, including atmosphere, orbital measurement error, antenna vibration, and scenery height fluctuation; moreover, because of the extremely large imaging swath, these undesired errors also have severe 2-D spatial variance. Thus, the autofocus processing plays a very important role in GEO SAR. However, current autofocus algorithms cannot handle all of the aforementioned complicated and 2-D spatial-variant errors simultaneously. In this article, an autofocus back projection (BP) method for GEO SAR based on minimum entropy is proposed. First, the BP algorithm based on a digital elevation model (DEM) is adopted to deal with the scenery height fluctuation. Then, the 2-D image segmentation is conducted to solve the spatial variance of the undesired errors. Subsequently, without the assumption of error type and considering both the amplitude error and phase error, the autofocus processing based on minimum entropy and adaptive moment estimation (Adam) is conducted to estimate the undesired errors iteratively and precisely. Moreover, the aperture division and sub-aperture fusion will also be utilized to alleviate the image quality degradation or even defocus, which could also improve the precision of error estimation. Finally, computer simulation results validate the effectiveness of the proposed method.

Linear-array multiple-input–multiple-output (LA-MIMO) synthetic aperture radar (SAR) can obtain 3-D radar images by only one pass. However, it is sensitive to time-variant measurement errors of curved track and time-variant attitude angles, meaning that autofocus processing for the LA-MIMO SAR tomography is necessary. The existing autofocus methods cannot be used to estimate thetime-variantand3-D space-variantmotion errors (3-D SVME) of the LA-MIMO SAR. To solve this problem, a new autofocus approach based on multiple local autofocusing and the LA-MIMO SAR time-variant motion error estimation is proposed. First, the local motion error estimation based on the fast local spectral analysis (SPECAN) 3-D imaging and the maximum contrast optimization 2-D local autofocusing is performed to estimate the local time-variant motion errors. Then, based on the linear-array motion error model, the time-variant 3-D trajectory deviations of the array center and attitude angles are estimated by the weighted least square estimation (WLSE) to solve the 3-D SVMEs. Last, the 3-D fast factorized backprojection (FFBP) is performed to obtain the well-focused 3-D image of the whole beam. The proposed approach has been applied for the tomography of a new crawler-type unmanned-ground-vehicle (UGV) LA-MIMO SAR. Both the simulation and real data experiments verify the effectiveness of the proposed approach.

A distributed configuration is a promising realization of tomographic synthetic aperture radar (TomoSAR) 3-D imaging. It is able to implement single-pass tomographic imaging in a very short time and, hence, outperforms the traditional time-consuming multipass TomoSAR. It also outperforms the traditional single-platform TomoSAR by achieving a higher resolution in elevation by forming a much larger spatial baseline. However, there has been little research reported on the distributed TomoSAR so far. In this article, we, for the first time, experimentally demonstrate the great potential of the single-pass distributed TomoSAR 3-D imaging. The main contributions are threefold. First, a new distributed TomoSAR 3-D imaging model is built, characterized by using both inner monostatic and bistatic spatial configurations. Second, a new multistatic synchronization scheme is developed for accurately correcting both multistatic time and phase synchronization errors. Finally, a P-band distributed unmanned-aerial-vehicle (UAV) TomoSAR prototype with four separate stations is built with an elaborately designed time-division waveform for full data acquisition. To the best of our knowledge, this is the first distributed TomoSAR system. We have also implemented the first single-pass TomoSAR 3-D imaging experiment and successfully achieved a meter-level 3-D image in Pinggu, Beijing, China.

Tomographic synthetic aperture radar (TomoSAR) is used to achieve three-dimensional imaging. Existing methods mainly focus on super-resolution without ambiguity. However, the baseline is uncontrollable in ground-based lunar mapping, which leads to long sparse non-uniform baseline with altitude ambiguity. To suppress the altitude ambiguity, this paper proposes an innovative dual-frequency de-ambiguity method. The mechanism is to enhance real targets and suppress the spurious targets by multiplying the images at different frequency points. Specifically, genetic algorithm (GA) is applied to achieve the optimum frequencies with the lowest peak sidelobe ratio. The computer simulation and real data experiments verify the effectiveness of the proposed approach.

Main approaches of observing asteroids rely especially on ground-based optical telescopes, yet not capable of detecting detailed structures of the surface. However, ground-based radar observations can provide delay-Doppler images which help to reconstruct a more accurate shape model. To form the shape model of asteroids, we propose a shape reconstruction method via multi-aspect delay-Doppler radar images and describe the surface through spherical harmonic coefficients. In order to reconstruct shape model as accurate as possible, an optimal solution of the coefficients is required. Three traditional optimization algorithms are applied and performances are compared. When positive decision is made, suitable parameters can achieve effectively with these algorithms followed with an accurately recovered shape of asteroids. Numerical simulations have been conducted to demonstrate the effectiveness of the proposed method.

The extremely-high-squint spotlight synthetic aperture radar (SAR) has been widely used in military and commercial fields. Due to the extremely-high-squint angle, the point spread function (PSF) used for SAR resolution evaluation suffers from severe distortion and thus results in non-uniform spatial resolution. To solve this problems, this paper proposes a novel radar parameter adjustment (RPA) method to correct the PSF distortion during the data acquisition. The mechanism is to achieve an orthogonal PSF by correcting the wavenumber spectrum distortion. In this case, the radar parameters, i.e., the center frequency and the chirp rate, vary at every azimuth sampling position. The proposed approach is validated via the computer simulations.

Yaw steering is an important technique for bistatic spaceborne synthetic aperture radar (SAR), traditionally implemented separately at the master and slave satellites, such as the X-band TerraSAR-X/TanDEM-X system. This separate yaw steering method, however, will degrade the illumination synchronization between the master and slave satellites. With the increment in frequency band and master–slave distance, the degradation will get worse, leading to intolerable azimuth resolution degradation or even failure of imaging. To solve this problem, a new joint yaw steering (JYS) method is proposed for bistatic spaceborne SAR with an arbitrary configuration in this letter. The word “joint” means that the master and slave satellitescooperativelywork to always make their beams point to an identical point on the Earth’s surface. In this way, the best illumination synchronization can be achieved, contributing to the best available azimuth resolution. The presented approach has been evaluated through computer simulations.

The spaceborne stripmap range sweep synthetic aperture radar (SS-RSSAR) is a new operation mode for efficiently imaging the regions squinted with satellite orbit. By nonuniformly implementing azimuth sampling, the SS-RSSAR is able to avoid the problem of transmission blockage when the slant range varies seriously. However, this advantage is achieved at the cost of inducing time-varying nadir echo and makes the valid and the nadir echoes inseparable in the time domain. In this study, we propose to suppress the nadir echo of the SS-RSSAR via waveform diversity on transmission. The key mechanism is to always make the valid and the nadir echoes in the same pulse interval have different waveforms characterized by high autocorrelation but low cross correlation performances. In this way, the nadir echo can be effectively suppressed by performing matched filtering with respect to the valid echo. The main contributions of this study are twofold: the analyses for the lowest required waveform number and the strategy of achieving sufficient waveform patterns. The validity of the presented approaches is evaluated via the extended-target computer simulations.

Applying a continuous varying pulse interval (CVPI) sequence is of significance for an extremely high-resolution (EHR) spaceborne sliding spotlight synthetic aperture radar (SAR) for complete data acquisition. An optimum CVPI sequence should be the one leading to the minimum movement of the echoes of interest in a pulse train. However, such an optimization can be hardly conducted using the traditional timing diagram. Aiming at solving this problem, this article introduces a new graphic tool, referred to as the strip layering diagram, to achieve an optimum CVPI sequence for an EHR spaceborne sliding spotlight SAR. The basic rationale is to explicitly employ a new parameter$\eta $to directly control the movement of the echoes of interest in a pulse train. The new strip layering diagram consists of two layered subdiagrams: one is the candidate subdiagram, consisting of the candidate strips presenting all the CVPI sequence candidates restricted by the data acquisition geometry; the other is the feasibility subdiagram, consisting of the feasibility strips marking all the feasible regions where the echoes of interest are not submerged by the transmitted pulses or contaminated by the nadir echoes. An optimum CVPI sequence can be accurately and efficiently achieved by sketching a line segment satisfying four conditions inside the strip layering diagram. The detailed comparisons between the new diagram and the traditional timing diagram are also provided, followed by computer simulations to validate the presented approaches.

The compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods have emerged as the standard approach to obtain super-resolution (SR) SAR images and achieve extraordinary performances. However, they face three challenges. First, this kind of method is mainly based on the point scattering model and not suitable for characterizing the line-segment-scattering and surface-scattering features of distributed targets. Second, the hyperparameters in these methods are hard to tune to optimal values. Third, due to a large amount of calculation, these methods are difficult to apply in practice. In this article, to solve these problems, we introduce the line-segment-scatterers (LSSs) and rectangular-plate-scatterers (RPSs) in SAR echo model to develop the SAR hybrid echo model and propose two SAR parametric SR image reconstruction methods based on solving a CS problem, where three penalties are utilized to exploit the sparsity of the point scatterers, LSSs, and RPSs, respectively. At the core of the first method is a direct solver called multicomponent alternating direction method of multipliers (MC-ADMM) solver that solves the CS problem quickly and iteratively based on closed derivative expressions. In contrast, the second method maps the MC-ADMM solver into a deep unfolded neural network, i.e., the parametric SR imaging network (PSRI-Net), which is faster, and the parameters can be automatically set to the optimum. Since all the parameters of the MC-ADMM solver are learned discriminatively through end-to-end training in PSRI-Net. Extensive simulation and practical experiments are carried out to demonstrate the effectiveness of the proposed methods.

The edges of a target provide essential geometric information and are extremely important for human visual perception and image recognition. However, due to the coherent superposition of received echoes, the continuous edges of targets are discretized in synthetic aperture radar (SAR) images, i.e., the edges become dispersed points, which seriously affects the extraction of visual and geometric information from SAR images. In this article, we focus on solving the problem of how to recover smooth linear edges (SLEs). By introducing multiangle observations, we propose an SAR parametric image reconstruction method (SPIRM) that establishes a parametric framework to recover SLEs from SAR echoes. At the core of the SPIRM is a novel physical characteristic parameter called the scattering-phase-mutation feature (SPMF), which reveals the most essential difference between the residual endpoints of a disappeared SLE and points. Numerical simulations and real-data experiments demonstrate the robustness and effectiveness of the proposed method.

Linear array multiple-input multiple-output synthetic aperture radar (LA-MIMO SAR) tomography can provide 3-D radar images without layover and geometric distortion effects. However, suffering from the channel and motion error, and large computation, the real LA-MIMO-SAR data is difficult to be well focused quickly. Therefore, the 3-D imaging results of real LA-MIMO-SAR data are rarely shown in existing literature. In this paper, real LA-MIMO-SAR data imaging processing method based on 3-D fast factorized Backprojection is proposed to well focus the echoes with channel and motion errors, and large size of data. Then, our experiments of LA-MIMO-SAR 3-D imaging are demonstrated. The LA-MIMO radar is installed to a special tower crane and works in downward-looking and forward-looking geometry. Some 3-D imaging results are demonstrated to verify the downward-looking and forward-looking LA-MIMO-SAR modes and the processing method.

Synthetic Aperture Radar (SAR) tomography is an important technique for target elevation information inversion and reconstructs the 3D structure of the target via multi-pass observations. At present, SAR tomography is mainly used in large-scale, low-resolution scenes where the range between the radar and the target is far larger than the target size and the resolution only meters. Therefore, the high-order phase residue is small and will not affect the elevation recovery. However, in the case of the small-scale, high-resolution scenes, the traditional TomoSAR processing will introduce quadratic phase residuals and affecting the quality of recovery. In order to solve the problem, this paper proposes a new method to reconstruct the 3-D structure in high-resolution scenes via elevation segmentation recovery. The main idea is to establish a reference signal at different height sections of the target area so that the quadratic phase residual is less than π/2. Finally, the estimated result of each section is projectively transformed into a unified coordinate system to achieve stable and accurate recovery. Besides, the algorithm has been verified by simulation data and measured data.

The traditional synthetic aperture radar (SAR) is an active acquisition instrument that generates radiation and captures signals backscattered from the illuminated scene. However, when the phases of each reflection path are highly similar, the received echoes may sum in a destructive way. In this way, only a few strong scattered points of targets can be preserved in the traditional images. To recover the disappeared geometric features of the targets in SAR images, this paper proposes a parametric imaging method for circular-plate targets. Through analyzing the scattering models of various targets, the scattering feature of the circular-plate target in image domain is first presented. And by identifying the target type in image domain, the geometry information lost in the traditional image is recovered based on the scattering models. Numerical simulations have been conducted to demonstrate the effectiveness of the proposed method.

The spaceborne stripmap range sweep synthetic aperture radar (SAR) (SS-RSSAR) is a new-concept SAR mode proposed to efficiently image regions of interest (ROI) squinted with respect to satellite orbit. The most distinct feature of the SS-RSSAR is the beam sweeping in the elevation for continuous squinted ROI illumination. A wide nonlinear chirp scaling algorithm (WNLCSA) has been proposed to directly implement image formation along the ROI spreading direction. However, the algorithm efficiency is limited due to its time-consuming time-domain interpolation. In this letter, a new interpolation free WNLCSA (IF-WNLCSA) is proposed for more efficient SS-RSSAR imaging by substituting the interpolation with a much faster chirp scaling approach. Different from the interpolation, the chirp scaling induces additional range migration and Doppler modulation, both of which, however, are neglected due to their weak influences on imaging. The derivation of the chirp scaling is presented in detail, followed by the analyses on the algorithm accuracy and efficiency. The presented approach is evaluated by both the point- and extended-target simulations.

Traditional SAR imaging methods are based on point target scattering model, hence are not capable of recovering the shapes of distributed targets such as cylinders. The cylinder target is usually shown as two endpoints in the traditional single-angle SAR images. However, the distributed SAR can provide multi-angle observation information of the cylinder target. To improve the SAR image quality, we propose a distributed SAR imaging method for the cylinder target with a sparse distributed SAR configuration. The proposed method reconstructs the cylinder target by estimating the parameters from the SAR image, and then identity the cylinder target from the distributed SAR echo. When positive decision is made, the shape of the cylinder target can be recovered with these parameters. Numerical simulations have been conducted to demonstrate the effectiveness of the proposed method.

Synthetic Aperture Radar (SAR) ship detection has been a research hotspot and is significant for marine surveillance. Traditional constant false alarm rate (CFAR) detector has the disadvantages of high false alarm and poor adaptability. Deep learning provides a unique solution for SAR ship detection. However, the traditional deep network cannot reach very deep thus the accuracy is limited, and the training speed is slow. In this paper, a very deep network ResNet with higher accuracy and faster training speed is applied to train the SAR ship detection model. Moreover, transfer learning is applied to combat the small dataset. The proposed method is tested on a general SAR ship dataset and achieves 94.7% average precision. Comparative experiments show that our method has the best performance and which verifies the effectiveness of our method.

Image coregistration is a key step in tomographic SAR (TomoSAR) signal processing, and the quality of coregistration directly determines the tomographic results. Traditional coregistration is performed based on the far-field assumption, where the scattering characteristics remain constant with different look angles and distances. However, for near-field TomoSAR observation, the traditional coregistration method will fail because of the mismatch caused by the change of scattering characteristics. In this study, we propose a new structure-based coregistration method for near-field ground-based MIMO TomoSAR. Corse coregistration based on the structure and fine coregistration combining correlation function and singular modification are conducted to achieve coregistration accurately and robustly. The validity of the presented approach is validated by real data.

The application of the spaceborne stripmap range sweep synthetic aperture radar (SS-RSSAR) is limited by its fixed azimuth beam pointing. In this paper a more advanced spaceborne azimuth-range sweep synthetic aperture radar (ARSSAR) is proposed to illuminate the region of interest (ROI) by simultaneously sweeping the beam in range and in azimuth. In this case, more balances of the azimuth resolution and the azimuth swath can be flexibly realized. The basic data acquisition problem for the spaceborne ARSSAR is analyzed from two aspects: firstly, an optimized beam illumination strategy is achieved by minimizing the width of the echoes from the ROI-strip; secondly, a continuous varying pulse interval (CVPI) method is suggested to avoid the transmission blockage and maximize the allowable width of the ROI-strip. The presented approach is evaluated by the simulations.

Among high-squint high-resolution (HSHR) synthetic aperture radar imaging algorithms, nonlinear chirp scaling algorithm (NLCSA) and its extensions, such as extended NLCSA (ENLCSA), have a common drawback in that they all neglect the spatial variations of linear range migration (LRM) and Doppler centroid, and thus, only targets in a specific central slant range plane can be strictly focused. In this letter, we show that by using a new NLCSA, targets can be focused in the ground plane under HSHR conditions. Based on a more accurate 2-D spectrum, the new NLCSA outperforms the ENLCSA by introducing a new range–Doppler domain interpolation to correct residual range migration and a new perturbation function to remove the dependence of Doppler phase on azimuth. The coefficients of the new perturbation function are numerically calculated and then smoothed by polynomial fitting. Though the outputs of the numerical calculation are somewhat unstable at the current stage, it has been demonstrated to perform better than the algorithms neglecting the spatial variations of LRM and Doppler centroid, such as the ENLCSA, by point target simulations.

This letter theoretically extends and applies the overlapped subaperture algorithm (OSA) for the challenging spotlight synthetic aperture radar (SAR) imaging in a quasi-forward-looking (QFL) geometry. A parameter-adjusting framework, in which the radar parameters relate closely to the geometry, is employed to mitigate the spatial signal coupling. By innovatively fitting the phase error induced by the planar wavefront assumption directly to the wavenumbers, both the linear and quadratic phase errors, which determine the geometrical distortion and the defocusing effects, respectively, can be accurately expressed and then corrected. When compared with the parameter-adjusting polar format algorithm proposed by the authors in previous work, the OSA is superior in contributing to an expanded imaging swath. The validity of the OSA methodology is demonstrated by the simulation of a Ka-band SAR working in the QFL diving mode with an extreme 85° squint angle.

The spaceborne stripmap range sweep synthetic aperture radar (SS-RSSAR) is a new concept spaceborne SAR system that images the region of interest (ROI) with ROI-orientated strips, which, unlike the traditional spaceborne SAR, are allowed to be not parallel with the satellite orbit. The SS-RSSAR imaging is a challenging problem because echoes of a wide region have strong spatial varieties, especially in high-squint geometries, and are hard to be focused by a single swath. The traditional imaging algorithms could solve this problem by cost-ineffectively dividing an ROI into many subswaths for separate processing. In this paper, a new wide nonlinear chirp scaling (W-NLCS) algorithm is proposed to efficiently image the SS-RSSAR data in a single swath. Comparing with the traditional nonlinear chirp scaling algorithm, the W-NLCS algorithm is superior in three major aspects: the nonlinear bulk range migration compensation (RMC), the interpolation-based residual RMC, and the modified azimuth frequency perturbation. Specifically, the interpolation for the residual RMC, the most significant step in achieving the wide-swath imaging performance, is made innovatively in the time domain. The derivation of the W-NLCS algorithm, as well as the performance analyses of the W-NLCS algorithm in aspects of the azimuth resolution, accuracy, and complexity, are all provided. The presented approach is evaluated by the point target simulations.

The polar format algorithm (PFA) is a classic algorithm for processing spotlight synthetic aperture radar (SAR) data and is extensively used in practical applications nowadays, mainly because its well performance in processing highly squinted data. Based on the chirp Z-Transform (CZT), PFA can be implemented accurately without any interpolation in the broadside case; however, for the squint mode case, since the input data are not uniformly distributed with same sampling intervals, the CZT algorithm requests interpolation in the azimuth dimension to obtain the uniform sample spacing, which leads to phase and amplitude error. To improve the processing accuracy under the squint mode, a CZT-based continuous varying PRF PFA is introduced. It achieves uniformly scaled grid along azimuth direction in wavenumber domain; as a result, interpolation in both range and azimuth dimensions can be avoided.

High resolution imaging is an important trend for SAR. The wide bandwidth signal is a must for high resolution. The error of traditional model based on the narrow bandwidth system may be inevitable for high resolution imaging. In this paper, a general SAR echo model is derived for the wide bandwidth system, and we also get the error factor between the traditional and general model. By simulating the two models, the conclusion is derived that if the transmitted signal is LFM waveform, the target is single and the receiving antenna is unit weight, the error of the two models can be neglected. The general echo model in more complicated situations is in the ongoing research. This study will make for the further research of high resolution SAR imaging algorithm.

This paper analyzes an innovative geographically referenced (GR) stripmap mode for the airborne synthetic aperture radar (SAR). In the GR stripmap SAR, the antenna beam illuminates orthogonally with the ground strip, which is not parallel to the SAR trajectory. Benefiting from the GR stripmap mode, the effective observation swath can be enlarged comparing to what can be realized by the traditional stripmap SAR. A modified nonlinear chirp scaling (MNLCS) method is suggested for the GR stripmap SAR imaging. It can solve the problem caused by scatterers' non-uniform Doppler history that disables most traditional imaging algorithms for the GR stripmap SAR. The MNLCS algorithm consists of three main steps. First, a bulk range migration compensation procedure eliminates the linear range migration (range walk). Second, an azimuth perturbation filter unifies the Doppler frequency modulation rate for each range cell. Lastly, a modified chirp scaling processing finishes the data focusing. Performance of the MNLCS algorithm was analyzed, followed by a series of computer simulation results, which validated the MNLCS for the GR stripmap SAR imaging.

The polar format algorithm (PFA) is a wavenumber domain imaging method for spotlight synthetic aperture radar (SAR). The classic fixed-parameter PFA employs interpolation technique for data correction. However, such an operation will induce heavy computational load and cause degradation in computation precision. To optimize image formation processing performance, this study presents a novel parameter-adjusting PFA, which can implement SAR image formation at an extremely highly squint angle with obviously improved computation efficiency and imaging precision. In the parameter-adjusting PFA, radar parameters, such as center frequency, chirp rate, pulse duration, sampling rate, and pulse repeat frequency (PRF), vary for each azimuth sampling position. Due to the parameter adjusting strategy, the echoed signal can be acquired directly in keystone format with uniformly distributed azimuth intervals. In this case, range interpolation, which is necessary in the fixed-parameter PFA to convert data from polar format to keystone format, can be eliminated. Chirp z-transform (CZT) can be employed to focus SAR data along the azimuth direction. Compared with truncated sinc-interpolation, CZT was found to perform better in inducing less phase and amplitude errors in data processing. When residual video phase (RVP) compensation was accomplished for dechirped signal, the processing steps of the parameter-adjusting PFA were simplified as azimuth CZTs and range inverse fast Fourier transforms (IFFT). Lastly, computer simulation of multiple point targets validated the presented approach.

The interpolation-based Polar Format Algorithm (PFA) can be used in bistatic Synthetic Aperture Radar (SAR) image processing while suffering from heavy interpolation computation load and complicated space-dependent resolution. To decrease computation load, this paper presents a nonlinear trajectory-based PFA, in which sensors are designed to fly on conical surface to avoid range interpolation. Due to this conical bistatic geometry, two advantages can be achieved. First, part of computation load can be converted to navigation system and processing speed can be improved. Second, a space-independent range resolution can be approached. A following multi-scatter simulation validates the presented approach.