Phase filtering is one of the core signal processing steps in interferometric synthetic aperture radar (InSAR). In recent years, InSAR phase filtering algorithms have evolved from traditional solutions to deep learning (DL) methods, significantly improving the processing efficiency. However, most DL-based phase filtering techniques originate from optical filtering methods, and these methods inevitably entail a tradeoff between noise suppression and detail preservation. To resolve this contradiction and fully take into account the characteristics of InSAR phase, a multiscale phase filtering network (MPFNet) based on multilook information fusion is proposed. First, the network adopts the multiscale structure to balance noise suppression and detail preservation, where the multiscale information is obtained through multilook interferograms of varying numbers of looks. Second, drawing on the mechanism of super-resolution, the network incorporates the residual feature distillation blocks (RFDBs) to restore the scale of interferograms. Finally, in response to the demand for complex phase filtering, a loss function based on cosine similarity is constructed, which avoids the discontinuity at$\pm \pi $affecting the filtering results. Computer simulation and experiments based on real InSAR data verified the effectiveness of the proposed method.

Lunar wide-swath imaging with Earth-based radar is of great significance for lunar scientific research. Conventional Earth-based radars with high frequencies and large-aperture antennas usually operate in the spotlight mode, resulting in a relatively narrow imaging swath. With the increasing demand for large-scale mapping of the lunar surface, it is necessary to develop a kind of wide-swath imaging mode. Hence, this paper proposes a novel imaging mode, namely terrain observation by beam steering (TOBS) mode. The key feature of TOBS mode is to steer the antenna beam at a non-uniform angular speed along the geographical orientation of the scene swath during the data acquisition. The key techniques are: 1) a dynamic beam control method is proposed to achieve uniform azimuth resolution; 2) a variable PRF design method is proposed for effective data acquisition; 3) an improved ground Cartesian back-projection (GCBP) algorithm based on affine geographic coordinate (AGC) system is proposed for efficient TOBS mode imaging. This paper reports the first demonstration of the TOBS mode lunar imaging with an Earth-based radar prototype system. A scene swath of about 750 km has been imaged utilizing the TOBS mode with an azimuth resolution of about 25 m, and the effectiveness of the proposed method is successfully validated.Unlike the traditional "mosaic mode", where multiple separate observations are required to obtain a long swath image leading to reduction in efficiency and enhanced complexity of data processing. The TOBS mode provides a new technical approach for lunar wide-swath imaging utilizing large-aperture and high-frequency Earth-based radars, translating to high-resolution lunar image datasets.

The back-projection (BP) algorithm has been regarded as a robust high-resolution imaging algorithm, particularly suitable for Earth-based radar lunar imaging. To offset phase errors induced by non-ideal effects, autofocus is essential to obtain well-focused lunar images. However, traditional back-projection autofocus algorithms are often computationally burdensome under long synthetic aperture time. In this paper, an effective back-projection autofocus algorithm is developed for Earth-based radar lunar imaging. To ensure that the defocusing caused by phase error lies in the azimuth direction of the BP image, the delay-Doppler coordinate system is introduced into the BP algorithm, and lunar images are generated on the delay-Doppler grid arranged on the lunar surface. Then, the Fourier transform relationship between the BP image and its wavenumber spectrum is established to facilitate the application of phase gradient autofocus (PGA) algorithm to the BP image. To further improve the estimation accuracy of phase error, the spectral characteristics are analyzed in detail. A multi-subband autofocus method based on local scene is proposed to mitigate frequency-dependence of phase error and space-variance of wavenumber spectrum. The processed results of real lunar data validate the effectiveness and efficiency of the proposed algorithm.

A ground-based radar is a potential technique for lunar surface imaging. However, due to the Earth’s rotation, the maximum azimuth resolution is limited for a single observation. To solve this problem, this letter analyzes the feasibility and performance of obtaining multiangle data from different observations and forms large virtual apertures through aperture synthesis. To ensure the quality of synthesized images, an observation baseline selection method is proposed based on the principle of spectrum continuity, specifying that for two noncontinuous observations, there should be a point on each of them whose target-to-radar vectors share the same direction. Besides, an aperture synthesis algorithm based on spectrum compression is proposed to eliminate spectrum aliasing. The effectiveness of the algorithm is verified via computer simulations and real data experiments. By forming a well-focused 500-m resolution image from two noncontinuous observations, the validation of the proposed algorithm has been proved.

Ground-based distributed radar is a potential technique for space target detection. However, in the case of low signal-to-noise ratio (SNR), it is difficult to achieve long-term integration due to the limited ephemeris guidance accuracy and complex motion model. To solve this problem, a space target detection algorithm based on digital beamforming (DBF) and generalized Radon-Fourier transform (GRFT) is proposed in this paper. To avoid the gain loss caused by ephemeris errors, small-scale beam-searching is conducted through DBF technique, which also enables the measurement of target angle and even angular velocity. Besides, transforming the problem of energy accumulation into parameterized model matching, the GRFT process can achieve long-term integration effectively in the case of complex motion models. The effectiveness of the algorithm is verified via real data experiments based on a ground-based distributed radar. By showing an effective 30-second integration and a computational efficiency improvement of 40%, the validation of the proposed algorithm has been proved.

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.

Earth-based radar is a pivotal instrument in deep space exploration to obtain radar images of desired celestial bodies. However, the system performance and image resolution of conventional integrated Earth-based radars with only one radar are limited by the power-aperture product and cannot meet the higher demands of deep space exploration. Distributed coherent radar is a new radar system composed of multiple radar units and a central control system, and its system performance can be further improved by increasing the number of radar units. Distributed coherent radar provides a reliable way to build a high-performance and high-resolution Earth-based deep space exploration system. This article introduces several key technologies about distributed coherent radar astronomical imaging: 1) high-precision coherence parameter estimation, which ensures full-coherence performance of the distributed coherent radar; 2) high-precision nonideal effect compensation, which eliminates the image offset and defocusing induced by the nonideal effects; 3) fast factorization backprojection (FFBP) algorithm, which achieves high-resolution fast imaging of celestial bodies. Moreover, based on a distributed coherent radar prototype system composed of four radar units with an antenna aperture of 16 m, high-resolution imaging experiments of the moon are conducted, and the effectiveness of the distributed coherent radar is successfully validated, which could not only provide a reference for the distributed coherent radar system but also provide a reliable solution for detection and imaging of other celestial bodies in the solar system in the future.

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.

Due to the limited accuracy of navigation systems, airborne InSAR processing is affected by residual motion error (RME). The phase-based methods such as multi-squint are usually based on the assumption that the RME is quite small. Different from dual-antenna InSAR systems, larger RME occurs in repeat-pass InSAR experiments since the RMEs are independent between two images, which causes decorrelation and reduces compensation accuracy in the phase-based method. With the improvement of SAR image resolution in recent years, the accuracy of co-registration is also improved. In this paper, a method for estimating the residual rotation angle of radar trajectory based on co-registration offset is proposed to compensate the RME when facing decorrelation. After that, the entire process flow of compensating RME is given. The computer simulation results have been conducted to verify the effectiveness of the proposed approach.

The 3-D inverse synthetic aperture radar (ISAR) imaging technology is widely used for noncooperative targets, which can obtain precise topography, structure, and rotation information of celestial target. However, celestial target observation has the features of long observation distance, low echo signal-to-noise ratio (SNR), and complex rotation characteristic, which severely degrades the performance of traditional 3-D ISAR methods. Therefore, in order to realize high-precision 3-D ISAR imaging of celestial target, a parametric 3-D ISAR imaging method is proposed in this article. First, a 3-D imaging model is established based on the complex rotation characteristic of the celestial target, which indicates that the rotation vector and polar diameter estimation is the key to 3-D reconstruction. Second, in order to overcome the performance degradation of traditional 3-D ISAR methods under low SNR, a parameter estimation method based on hybrid generalized radon-Fourier transform (HGRFT) is proposed, and the core is to use GRFT within subaperture and noncoherent accumulation between subapertures to achieve hybrid accumulation of echo signals, so as to obtain the desired rotation vector and polar diameter fast and accurately. Consequently, the 3-D reconstruction of the celestial target under low SNR can be achieved based on the 3-D imaging model and the parameter estimation results. Moreover, two fast implementations based on parameter search space dimensionality reduction and heuristic search, respectively, are proposed, which can reduce the computational load of high-dimensional space parameter estimation and further improve the algorithm efficiency. Finally, the proposed method is validated by celestial target 3-D ISAR imaging simulation.

Spaceborne multi-channel synthetic aperture radar (SAR) is an effective means to realize high-resolution and wide-swath imaging. However, for spaceborne multi-channel SAR imaging of maritime moving targets, the target motion will cause undesired channel imbalance, i.e., phase error, and further introduce the spurious targets in the image. To solve this problem, this paper proposes a novel spaceborne multi-channel SAR imaging algorithm for maritime moving targets, which consists of sequential coarse imaging and accurate imaging. The key strategies are to separate different moving targets by coarse imaging and to estimate the phase error based on the relationship between phase errors and amplitudes of spurious targets. First, the quantitative relationship between phase errors and amplitudes of spurious targets is established. Second, based on coarse imaging results, different maritime targets are effectively separated. Then, based on the measured amplitudes of spurious targets, a cost function, which represents the difference between the real target velocity and estimated target velocity, is constructed and minimized to separately estimate the velocities and phase errors of targets. Moreover, to further improve the accuracy of estimation and to suppress the undesired effects caused by target defocusing, clutter, and noise, an iterative strategy is adopted. Last, by auto-focusing, a well-focused SAR image is obtained. The GF-3 dual-channel real data experiment is conducted. The results indicate that the spurious targets are well suppressed, which validates the effectiveness of the proposed algorithm.

Most research on sparsity-driven synthetic aperture radar (SAR) imaging has been carried out in$\ell _{1}$-norm regularization and considers that the SAR image contains only targets and noise, which ignores the clutter and seriously degrades classical algorithms. To address this problem, we propose an integrated detection and imaging algorithm for radar sparse targets with constant false alarm rate (CFAR) regularization by alternating direction method of multipliers (ADMM), called CFAR-ADMM, and we further introduce total variation (TV) regularization and propose the more robust CFAR-TV-ADMM. First, a more complete echo signal model, which considers targets, the clutter, and the noise simultaneously, is established. Then, inspired by the CFAR detection, a novel regularization with sparse target awareness is proposed. The proposed regularization can obtain the statistical characteristics of clutter and noise region by region, and distinguish whether the current cell contains the target effectively and accurately. Benefiting from this novel regularization, CFAR-ADMM and TV-CFAR-ADMM can not only realize the sparse imaging but also detect sparse targets simultaneously, which can reduce the propagation error caused by cascading processing and improve the solution accuracy. Finally, the proposed algorithm is verified by simulation data results, phase transition analysis, and real data experiments.

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.

Differential SAR interferometry (DInSAR) is a useful technique used to measure small movements and surface deformation. However, ionospheric phase screens are a major error source in multipass terrain observation by progressive scans sar (TOPSAR) interferograms. In this letter, an improved split-spectrum method is proposed. First, the burst used for ionospheric phase estimation is selected through coherence, and then, the ionospheric phase of the burst is estimated based on the split-spectrum method. Finally, the TOPSAR ionospheric space-variable phase in a large scene is obtained through 2-D space-variable fitting, which avoids the complicated processing of splicing between bursts of different periods and reduces the number of unwrapping calculations for large scenes after splicing. The method can ensure that the number of calculations is reduced without loss of accuracy. Sentinel-1 TOPSAR real data processing verifies the correctness of the proposed method.

Distributed multichannel synthetic aperture radar (MC-SAR) is a system in which transmitting or receiving arrays are distributed on multiple platforms or at different locations on one platform. The along-track component of the baseline makes distributed SAR promising in high-resolution wide-swath (HRWS) imaging such as azimuth MC-SAR. However, the additional channel mismatch introduced by the cross-track baseline (CTB) is considered for the distributed SAR. When the azimuth beam is wide, the azimuth-variant channel mismatch caused by the CTB must be compensated before SAR imaging. First, an improved azimuth signal reconstruction algorithm for distributed wide-beam SAR is proposed in this paper. The azimuth variance of the channel mismatch is considered in a reconstruction filter to further suppress the ambiguity, and the computational consumption is decreased by approximately decomposing the mismatch matrix. Second, the ambiguity suppression performance of the proposed method is analyzed quantitatively. Finally, a simulation and real data processing are provided to demonstrate the effectiveness of the proposed method.

Radar imaging for ships is hard because of the unpredictable motion states of ships. Existing ship radar imaging methods usually do not take the effects of different motion states into account, which leads to a degraded imaging result when the utilized imaging algorithm cannot match the target motion state. To solve this problem, a motion state judgment and radar imaging algorithm selection method is proposed, whose keys are to estimate the motion parameters of scatter points on the ship, judge the target motion state based on the space-variant features of the estimated motion parameters, and further choose a proper imaging algorithm to achieve the radar imaging result with higher quality. In this article, the radar imaging model of ship is first constructed, and the spatial variance features of motion parameters are quantitatively analyzed. Next, an improved motion parameter estimation method utilizing generalized Radon–Fourier transform (GRFT) modified by sidelobe-learning particle swarm optimization (SSLPSO) and relaxation (RELAX) technique is proposed, which can solve the performance reduction caused by the unnecessary values introduced by the method based on traditional GRFT and realize accurate motion parameter estimation. Then, based on the estimated motion parameters, a motion state judgment and radar imaging algorithm selection method, which takes the targets’ motion state, theoretical resolutions, as well as the effect of high-order phase error into consideration, is proposed to obtain a high-quality and high-resolution radar image. Finally, computer simulation and experimental results of GaoFen-3 (GF-3) satellite single channel data validate the proposed method.

Translational compensation is a crucial step in inverse synthetic aperture radar (ISAR) imaging. However, nonparametric compensation methods may collapse under the condition of a low signal-to-noise ratio (SNR), and high-order parametric compensation methods usually have a large computational load. In this article, two cascaded integration methods are proposed to solve the problems above based on the generalized Radon-Fourier transform (GRFT). The first method models the translational motion as polynomial and utilizes the proposed subaperture GRFT (SAGRFT), which is a fast implementation of the GRFT, to estimate the translational parameters. The SAGRFT divides the full aperture into several subapertures and realizes coherent integration by implementing moving target detection (MTD) within subapertures and the GRFT among subapertures. In addition, the distribution property of the blind speed side lobes (BSSLs) generated by the SAGRFT is analyzed. On this basis, the second method based on metaheuristic algorithms is proposed to further accelerate the parameter estimation process and solve the BSSLs problem. The proposed methods can not only play a role in stealth target imaging but also be utilized in near-Earth asteroid (NEA) imaging in radar astronomy. Finally, the numerical simulations of asteroid imaging and the experimental results of plane imaging are demonstrated to verify the performance advantages of the proposed methods.

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.

High-squint imaging is an effective means to enhance the flexibility and coverage ability of spaceborne synthetic aperture radar (SAR). Although existing imaging algorithms based on linear range cell migration correction (LRCMC) and nonlinear chirp scaling (NCS) can reduce the range-azimuth coupling of the spectrum and the spatial-variant of the Doppler parameter to some extent, they become invalid as the resolution increases. On one hand, the beam rotation of sliding spotlight SAR results in nonlinear azimuth-variant of the Doppler centre, and the traditional deramping operation, which removes the linear variation, will cause spectrum aliasing. On the other hand, these algorithms assume the azimuth-variants of range cell migration (RCM) are consistent in the total swath. However, the azimuth-variants of RCM are different in different range cells, which cannot be neglected in high-resolution imaging. To solve these problems, a novel imaging algorithm based on two-dimensional spatial-variant range cell migration correction is proposed in this paper. First, LRCMC is utilized, and the nonlinear azimuth deramping operation is conducted to obtain aliasing-free spectrum. Then, the azimuth-variant of RCM is corrected by azimuth interpolation and polynomial compensation. Noting that the interpolation coefficient varies linearly with slant range, this can weaken the azimuth-variant differences of RCM in different range cells. Meanwhile, azimuth polynomial compensation can correct the consistent azimuth-variant of RCM, and hence the azimuth-variant of RCM can be totally corrected. Finally, the compression is performed via the range chirp scaling and azimuth NCS. The effectiveness of the proposed algorithm is verified by computer simulations.

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.

Translational motion compensation plays an important role in inverse synthetic aperture radar (ISAR) imaging. However, existing translational motion compensation algorithms cannot work well when the signal-to-noise ratio (SNR) is low and the target has complex motion at the same time, as the algorithms usually assume that these two situations do not occur simultaneously. To address this problem, an improved parametric translational motion compensation algorithm based on signal phase order reduction (SPOR) and minimum entropy is proposed. The key is to decrease the phase order of the signal, which has a nonlinear phase and corresponds to the complex motion, and then obtain the signal with a linear phase corresponding to the noncomplex motion. Subsequently, the signal is transformed into the Doppler domain to generate the SPOR result. Obviously, when the translational motion is well compensated, the SPOR result will be coherently accumulated and has the best quality, which means that the SPOR result has good robustness against the low SNR. Thus, the translational motion is modeled as a polynomial model, the entropy of the SPOR result is taken as the optimizing target, and the relationship between the translational motion compensation parameters and the entropy is established. Finally, coarse search and particle swarm optimization (PSO) are sequentially performed to optimize the entropy of the SPOR result and estimate the translational motion compensation parameters accurately and efficiently. Computer simulation results and experimental results based on unmanned aerial vehicle (UAV) radar validate the proposed algorithm.

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.

Earth-based repeat-pass synthetic aperture radar (SAR) interferometry (InSAR) is a powerful tool to obtain the high-precision lunar terrain and deformation information, due to the short revisit period as well as the all-weather, long-time, and large-coverage characteristics of radar lunar observation. In this article, the baseline formation mechanism is investigated. Theoretical analyses show that the interferometric baselines mainly come from the lunar libration; however, the lunar libration may also result in a significant change of baselines and further lead to serious geometric decorrelation. To address this problem and ensure the Earth-based repeat-pass InSAR of the Moon, the precise relative motion model between the radar and the lunar target, considering lunar libration, is established, and the subradar point (SRP) wagging phenomenon is analyzed. Then, to evaluate the effects of SRP wagging on InSAR coherence, a geometric decorrelation model based on azimuth angle and incident angle is proposed. Based on that, the spatial–temporal baseline for Earth-based repeat-pass InSAR of the Moon is analyzed based on numerical simulation. Results show that the temporal baseline has obvious periodicity with a cycle of about 27 days. When the temporal baseline is an integral multiple of 27, the spatial baseline usually has the minimum value and the geometric decorrelation effect is relatively slight. Moreover, using the geometric decorrelation criteria, we examine the effects of radar frequency, position, and target location on the quantity of viable interferometric pairs, which has guiding significance for the selection of radar system parameters, radar position, and lunar target area.

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.

Traditional synthetic aperture radar (SAR) moving target imaging algorithms usually deal with the enlovpe and phase of the signal seperately, which is under a high signal-to-noise ratio (SNR) condition. In this paper, an improved imaging method for moving targets based on GRFT under low SNR is proposed, and the Doppler ambiguity and motion parameter estimation can be solved simultaneously. Computer simulations are conducted to validate the effectiveness of the proposed method.

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 problem of estimating high-mobility channels is a special case of the general problem of recovering multi-dimensional (MD) complex sinusoids. This paper is concerned with estimation of multiple frequencies with prior knowledge from incomplete and/or noisy samples. Suppose that it is known a priori that the frequencies lie in some given intervals, we develop efficient super-resolution estimators by exploiting such prior knowledge based on frequency-selective (FS) atomic norm minimization. We study the MD Vandermonde decomposition of block Toeplitz matrices in which the frequencies are restricted to lie in given intervals. We then propose to solve the FS atomic norm minimization problems for the low-rank spectral tensor recovery by converting them into semidefinite programs based on the MD Vandermonde decomposition. We also develop fast solvers for solving these semidefinite programs via the alternating direction method of multipliers (ADMM), where each iteration involves a number of refinement steps to utilize the prior knowledge. Extensive simulation results are presented to illustrate the high performance of the proposed methods.

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.

The traditional synthetic aperture radar (SAR) sparse imaging methods are based on the point scattering model. However, this model is not suitable for many distributed targets with large variations in scattering characteristics at different angles, i.e., many distributed targets can no longer be considered as a combination of a series of ideal point scatterers under multi-angle observations. To solve this problem, by introducing the improved attributed scattering model into the traditional SAR echo model, we propose our multi-angle sparse image reconstruction method (MASIRM). Through modeling the illuminated scene with point scatterers and line-segment-scatterers, a multi-angle echo model is first presented. By generating an adaptive mixed dictionary and applying the pattern-coupled sparse Bayesian learning, the MASIRM obtains more geometric information of the distributed target with higher quality sparse SAR images. Real data experiments demonstrate that MASIRM performs favorably against traditional imaging methods.

Dual-channel synthetic aperture radar (SAR) system is promising in high-resolution wide-swath (HRWS) imaging. However, gain and phase unbalance in the dual-channel SAR system will severely degrade the performance. To make clear the channel error effect on the image of the reconstruction algorithm, a precise analysis of the channel error effect is proposed in this letter. Based on the channel error effect analysis, the amplitude and position of the ambiguity caused by the channel error can be calculated, which is significant for understanding the image quality degradation caused by channel unbalance. Finally, based on the Chinese first dual-channel spaceborne SAR system-GF-3, the validity of this letter is verified by the simulated and real data, respectively.

In this paper, we consider a passive radar system that estimates the positions and velocities of multiple moving targets by using OFDM signals transmitted by a totally un-coordinated and un-synchronizated illuminator and multiple receivers. It is assumed that data demodulation is performed separately based on the direct-path signal, and the error-prone estimated data symbols are made available to the passive radar receivers, which estimate the positions and velocities of the targets in two stages. First, we formulate a problem of joint estimation of the delay-Doppler of reflectors and the demodulation errors, by exploiting two types of sparsities of the system, namely, the numbers of reflectors (i.e., targets and clutters) and demodulation errors are both small. This problem is non-convex and a conjugate gradient descent method is proposed to solve it. Then in the second stage we determine the positions and velocities of targets based on the estimated delay-Doppler in the first stage. For the second stage, two methods are proposed: the first is based on numerically solving a set of nonlinear equations, while the second is based on the neural network, which is more efficient. The performance of the proposed algorithms is evaluated through extensive simulations.

Penetrating radar systems are widely used to image the objects that are buried inside mediums (such as walls, ground, and so on). However, due to the phase error caused by the refraction of mediums, the object images obtained by directly applying the imaging methods that ignore the existence of the mediums are defocused, which affects the recognition of small objects. Conventional focus imaging algorithms typically obtain a focused image by iteratively searching for optimal compensation, which is very time-consuming and can result in overfocusing. To solve this problem, a near-field phase cross correlation (PCC) focusing imaging algorithm is proposed in this article. First, a free-space imaging model is established to replace the unknown half-space (air-to-medium) imaging model. Then, by analyzing the relationship between the free-space and the unknown half-space imaging models, the phase error and a reference depth in free-space imaging model are determined. The focused image can then be obtained by compensating for the phase error, which is directly estimated by the proposed PCC method. The permittivity and object depth can then be estimated based on the slope of the phase error and the reference depth. Furthermore, the proposed algorithm can be extended to the multi-layered model in a straightforward manner. Extensive simulations and experiments are presented to validate the proposed methods. The results show that the proposed PCC algorithm can effectively compensate the phase error and obtain high-quality images, and the permittivity and object depth can be accurately estimated in single-layer medium cases.

Geosynchronous synthetic aperture radar (GEO SAR)-related technologies are being mature, and the first GEO SAR satellite is expected to launch in the next ten years. Under this circumstance, some equivalent experiments should be conducted at the current stage to validate some key characteristics or parameters, which could significantly increase the success possibility of the GEO SAR project. To validate the feasibility of GEO SAR imaging with long integration time, which is the most important and fundamental characteristic of GEO SAR, the first helicopter platform-based equivalent GEO SAR experiment with long integration time was performed in Qianxi County of China on May 22, 2019. The integration time of it is 80 s, which is carefully designed to maintain the consistence between itself and the integration time of the GEO SAR. Furthermore, the azimuth signal-to-noise ratio gain with long synthetic aperture time is analyzed. Moreover, the 2-D space-variant motion error introduced by the complex helicopter trajectory and the performances of different imaging algorithms are analyzed to choose the proper imaging algorithms; to overcome the flaws and unclarities of existing algorithms, some improvements are proposed to obtain the well-focused SAR image. What is more, the equivalence of this experiment is also analyzed detailedly to demonstrate the effectiveness of this experiment. At last, the imaging result with synthetic aperture time of 100 s and the comparison between itself and the optic photograph validate the success of this equivalent experiment and the feasibility of GEO SAR imaging with long integration time.

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.

In low-contrast synthetic aperture radar (SAR) images, the contrast between roads and surrounding objects is low; therefore, many false roads will be introduced in the process of extracting road networks. To solve this problem, a two-step road network extraction framework is proposed. In the first step, the edge information of the road is extracted using a linear detector. To reduce false edges, a false edge removal algorithm based on the directional information of the edges is proposed. In the second step, an improved region growing (RG) algorithm is proposed, which can substantially improve the integrity of the road network extraction compared with the traditional RG algorithm. Finally, the proposed algorithm is validated by GF-3 satellite SAR images.

Existing ship inverse synthetic aperture radar (ISAR) imaging algorithms are not applicable, when the signal-to-noise ratio (SNR) is low, for the translational motion that cannot be well compensated by existing algorithms. To achieve ship ISAR imaging with low SNR, a ship ISAR imaging algorithm based on the generalized radon-Fourier transform (GRFT) is proposed in this paper. Considering not only the rotational motion but also the translational motion between the radar and the ship, the proposed algorithm uses the GRFT to simultaneously compensate the time-variant range envelopes and the Doppler phase. Thus, the signal coherence is fully utilized, and the coherent integration of the ship's multicomponent echo signal is realized. Subsequently, to overcome the problem of the heavy computational load and improve the efficiency of the proposed algorithm, the scheme of cascaded GRFTs that consists of the coarse GRFT and the subsequent fine GRFT is adopted. The coarse GRFT with large search ranges and intervals is aimed at obtaining the real ranges of ship scatter points' motion parameters. Based on the coarse GRFT result, the fine GRFT with small search ranges and intervals is performed to efficiently obtain the coherent integration result. Then, based on the coherent integration result, the constant false alarm rate (CFAR) detection is performed to obtain the desired scatter points and their amplitudes and motion parameters, and the multicomponent signal is reconstructed. Finally, based on the reconstructed multicomponent signal, the high-quality instantaneous ship ISAR image can be obtained. Computer simulations and experiment results validate the effectiveness of the proposed algorithm.

Penetrating radar systems are widely employed to scan the objects that are placed behind or buried inside mediums (such as walls, ground, and so on). As the clutter is much stronger than the target echo, clutter removal must be performed before imaging. The moving average subtraction, spatial notch filtering, and singular value decomposition methods are commonly used to remove clutter. However, the drawback is that these methods eliminate some of the target spectrum information, which causes target energy losses and generates side lobes. To solve this problem, two spectrum recovery methods are proposed in this paper. The first method recovers the spectrum magnitude and phase via sinc interpolation and linear fitting, respectively, which is fast and suitable for real-time processing. Although the second method recovers the spectrum based on matrix completion with prior information, which is more accurate and more computational expensive. Extensive simulations and experiments are presented to validate the proposed methods. The results show that the proposed methods can improve various traditional clutter removal methods, the side lobes are clearly suppressed, and the signal-to-clutter ratio is significantly improved.

To realize real-time imaging for the spaceborne synthetic aperture radar (SAR) using the chirp scaling (CS) algorithm, high generation rates of phase factors and heavy computation loads are unavoidable. For example, updating phase factors continuously in the 2-D time or frequency space is difficult for generating phase factors for real-time imaging. To solve this problem, a modified CS algorithm based on updating phase factors regionally is proposed. Moreover, conventional floating-point arithmetic is unaffordable for real-time imaging onboard. Therefore, fixed-point processing is adopted. In the proposed imaging algorithm, the phase factors are updated only at some specified times and frequencies, and fixed-point operation is adopted. Furthermore, based on the paired echo theory and the finite word length error mathematical model, the effect of the proposed imaging algorithm on the SAR image quality is studied. Moreover, based on the above analysis and the experiments of Chinese HJ-1C and GF-3 spaceborne SAR practical measured data, a hardware system including digital signal processor and field-programmable gate array boards is constructed.

In order to reduce speckle noise and maintain targets' characteristics in SAR image with low signal to noise(SNR), an adaptive speckle reduction algorithm based on the wavelet threshold de-noising and the Forward and Back(FAB) diffusion equation for marine scenes is proposed. First, a new wavelet thresholding de-noising function which can avoid shortcomings of other wavelet de-noising function such as Hard-thresholding, Soft-thresholding and Firm-thresholding function is proposed to filter part of noise and reduce the impact of noise on the FAB operation. Second, how to select suitable constant parameters is analyzed and the improved FAB operation is used to eliminate the influence of discontinuous points generated in wavelet operation, suppress the residual noise and stress targets such as bright points and edge. The effectiveness of the algorithm is verified by the experiments with the GF-3 image.

The interferometric phases in highly sloped terrain have the characteristics of large fringe density, narrow width, low correlation, and under-sampling. The local fringe frequ- ency (LFF) is a criterion to evaluate the trend and magnitude of the local terrain gradient and can be employed to improve the quality of interferograms. The results of the traditional LFF estimation method can be affected by phase noise, and sometimes the phase unwrapping (PU) operation is also required for some local regions. When it comes to highly sloped terrain, the phenomenon of phase under-sampling may cause incorrectness in the absolute interferometric phase during the operation of PU and may then influence the accuracy of the whole estimation. In order to solve this problem, this letter proposes an extended maximum-likelihood method for LFF estimation based on the multifrequency interferometric synthetic aperture radar (InSAR) data. Through the differences in the LFF between the different frequency InSAR data, the estimation quality map is introduced to modify the large error in certain regions by local 2-D fitting and thus achieves a accurate estimation of LFF in highly sloped terrain. Finally, the estimated results of LFF are used to guide the process of phase filtering. Simulated data and real airborne dual-frequency InSAR data are both employed to validate this proposed method.

The detection of a power transmission tower in a synthetic aperture radar (SAR) image has been widely studied. However, few works consider the geometric features of the power transmission tower. In a high-resolution SAR image, the geometric features of the power transmission tower are more obvious and can be used to further reduce the false-alarm probability. In this letter, a new power transmission tower detection method is proposed, which takes into account the geometric features of the target and obtains lower false-alarm probability than the traditional methods. First, the polar coordinate semivariogram is proposed, which has the advantages of low computational complexity and high sensitivity to the shape of the targets. Then, a three-layer neural network is employed to detect the power transmission tower, taking the geometric features as the input vector. Finally, the validity of the proposed method is illuminated by the experimental measurement results of the airborne data with 0.5-m resolution.

Elliptical orbits are widely used in the system design of geosynchronous synthetics aperture radar (GEO SAR) to improve the temporal resolution. The orbit height variation in the elliptical orbit can cause the variation of nadir interference and beam illumination region. This shortens the available time of a designed pulse repetition interval (PRI) for a specific beam pointing, and multiple PRIs have to be used at different orbital positions. To lengthen the available time, a continuous PRI variation method is introduced in this paper. By varying the PRI with a constant time interval, the acquisition window and transmit interference changes with the variant beam illumination region, which loosen the constraint of the transmit interference. A design example of the proposed method is provided with significantly lengthened available time.

In this paper, a creative hybrid adaptive filtering algorithm is designed to combine a modified mode filter with an adaptive median filter for the suppression of interferometric phase noise which is a quality insurance of the following phase unwrapping operation. This combination provides the hybrid filtering algorithm with remarkable flexibility to terrain. At first, this paper presents a modified mode filter with an adaptive shortest sub-interval estimator. Then, it uses the mode filtering result as the variable local phase center of the median filter and improves it with an adaptive filtering window varying with the residue density and correlation coefficient. Finally, a hybrid adaptive filtering algorithm is proposed for the reduction of interferometric phase noise. The studies utilize the simulated Peak data and the real Ayers Rock data as data support. Through analyzing the phase residues and the PSD values of the filtering results, we finally prove the validity of this method.

The magnitude error and phase error (MEPE) in the transfer function of a stepped-frequency synthetic aperture radar (SAR) system results in a periodic MEPE in the synthesized wideband waveform, which induces the grating lobes in the high-resolution range profile. In this letter, a novel grating lobe suppression method based on the SAR image is proposed. In the paired-echo theory, a single sinusoidal term of the periodic MEPE in the frequency domain induces a pair of grating lobes in the time domain. Based on the magnitudes and phases of a strong scatterer and its grating lobes in the SAR image, the sinusoidal terms in the periodic MEPE can be estimated using the proposed method. By compensating for the estimated sinusoidal terms in the spectrum reconstruction, the corresponding grating lobes can be suppressed to the background level of the SAR image. The validity of the proposed method has been demonstrated using computer simulations and experiments based on real data.

The aircraft altitude relative to the illuminated region is a significant parameter for the geometric correction in squint mode synthetic aperture radar (SAR) imaging. Error in the parameter causes geometric distortion, which degrades the image quality. To circumvent this problem, a relative altitude estimator (RAE) is proposed based on the relationship between the relative aircraft altitude and the range variation of the Doppler centroid. The high performance of the RAE is investigated compared to the Cramér–Rao lower bound of the relative aircraft altitude. Finally, the effectiveness of the RAE in practical applications is verified by real data processing.

The challenge in the imaging of high-squint-mode synthetic aperture radar (SAR) mounted on maneuvering platforms is the azimuth dependence of both the range migration and the azimuth focusing parameters (the azimuth frequency-modulation rate and higher order coefficients), which are caused by range walk correction and acceleration. In order to accommodate the dependence, a modified subaperture imaging algorithm is proposed. Based on the fact that the azimuth times corresponding to the same Doppler frequency are different for targets in the same range gate, after making blocks in the azimuth frequency domain, the azimuth-dependent range cell migration correction is performed in the azimuth time domain for each block. Considering that the regions of support in the azimuth frequency domain are different for targets in the same range gate, the equalization of the azimuth focusing parameters is achieved by a new azimuth nonlinear chirp scaling method in the azimuth frequency domain. In order to verify the effectiveness of the proposed algorithm, the simulation of a point target array is presented. Furthermore, the real SAR data with a squint angle of 70° are processed, and high-quality images with a resolution of 1 m are provided.

For satellites in geosynchronous orbit (GEO) using synthetic aperture radar (SAR), the direction of the satellite velocity vector in the Earth-centered, Earth-fixed (ECEF) coordinate system varies widely in one orbital period. When the velocity vector is approximately parallel to the range direction at equatorial latitudes, the 2-D sidelobes of the generalized ambiguity function (GAF) on the ground are non-orthogonal, which results in a significant deterioration in the ground resolution. To improve the ground resolution, this study investigates an optimal resolution steering (ORS) method for GEO SAR. The ORS method differs from the total zero Doppler steering (TZDS) method, which attempts to minimize the Doppler centroid. In the ORS method, a beam pointing direction is derived based on the constraints of optimal ground resolution. Then, a 2-D roll-pitch strategy is derived to obtain small steering angles, and the beam is steered to the optimal beam pointing direction. Finally, the ORS method is verified with simulations.

Airborne synthetic aperture radar (SAR) raw data are affected by motion errors due to atmospheric turbulence and aircraft properties, and this makes motion compensation (MOCO) an essential step. In order to decrease the influence of variable motion information, a kind of MOCO approach called azimuth-subaperture processing is widely used, which causes the azimuth-signal aliasing in squint SAR mode. Usually, this aliasing can be ignored, since its effect on final results is not worth mentioning. However, when the squint angle grows, it becomes serious and drops the quality of final images severely. To address this problem, an improved MOCO approach is proposed in this paper. Its main idea is to calculate the extended number of subapertures and eliminate the azimuth aliasing by extending the subaperture length. After theoretic analyses, simulations and experimental results are provided to demonstrate our proposed approach.

Aiming at the structural feature and polarimetric property preservation in speckle reduction of polarimetric synthetic aperture radar images, an improved speckle reduction algorithm based on structural judgment and hybrid four-component polarimetric decomposition (HPD) is developed. To protect the structural feature, a new structural judgment based on edge masks and pixels' sort is used for dividing pixels into three structural categories: bright, dark, and common targets. To protect the polarimetric property, a new scattering classification with HPD is employed to divide further the dark and common targets into four scattering classes. HPD inherits the physical significance of Pauli and Yamaguchi decomposition with its advantage of less time consumption. After that, without bright targets, a dark or a common pixel centered in a sliding window is filtered. To achieve better filtering results, pixels in its same and three neighboring scattering clusters from the same structural category are selected to filter. The experimental results of the UAVSAR and RADARSAT-2 data illuminate the validity of our proposed method.

The determination of Doppler ambiguity number (DAN) is indispensable for the generation of high-quality synthetic aperture radar (SAR) images. An incorrect DAN leads to lower image signal-to-noise ratio and degradation in the system impulse response function. Based on the relationship between DAN errors and the image quality, a novel DAN estimation algorithm named image-quality-based Doppler ambiguity resolver is presented. In the proposed algorithm, the DAN-dependent linear range cell migration correction and DAN-dependent azimuth compression are carried out in subscenes to obtain 2-D compressed SAR images, which are further employed to estimate DAN via minimizing the entropy. The presented algorithm is more robust than conventional methods to cope with demands of both low- and high-contrast scene applications. In addition, the approach is computationally efficient because it can be properly applied to segments of range-compressed data in small size and only a few sets of short fast Fourier transforms are required. Finally, experiments over real data of airborne X-band and spaceborne C-band are carried out to demonstrate the performance of the proposed approach.

The phase gradient autofocus (PGA) algorithm is widely used for synthetic aperture radar (SAR) autofocusing. In practice, polarizations of electromagnetic waves result in phase error estimates with different precisions obtained by PGA, yielding distinctly focused images for different polarization channels after compensating the phase errors. In this paper, a new autofocusing technique for the polarimetric application is proposed. The algorithm employs the redundancy of phase error information among different polarization channels. The phase error estimate obtained by different polarization channels which optimizes the image quality with the highest image contrast is treated as the final estimation result. In comparison with the standard PGA, the performance of the proposed approach is demonstrated using real data from airborne SAR.

Doppler centroid is a key focusing parameter of Doppler beam sharpening (DBS) imaging. According to the locked relationship between Doppler centroid error and image entropy, a minimum-entropy Doppler estimator (MEDE) is proposed. The algorithm breaks the conventional algorithms' assumption that the shape of echo signal's power spectrum is symmetrical, and the proposed method can circumvent the problem of low estimation precision using conventional algorithms to process the echo signal from high-scene contrast. The new algorithm based on entropy minimization has high estimation precision in the case of low- and high-scene contrast. MEDE has been successfully applied to a DBS real-time imaging processor of airborne radar and verified by flight experiments.