The recently developed Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have shown encour-aging and impressive results for visual SLAM. However, most representative methods require RGBD sensors and are only available for indoor environments. The robustness of reconstruction in largescale outdoor scenarios remains unexplored. This paper introduces a large-scale 3DGS-based visual SLAM with stereo cameras, termed LSG-SLAM. The proposed LSG-SLAM employs a multi-modality strategy to estimate prior poses under large view changes. In tracking, we introduce feature-alignment warping constraints to alleviate the adverse effects of appearance similarity in rendering losses. For the scalability of large-scale scenarios, we introduce continuous Gaussian Splatting submaps to tackle unbounded scenes with limited memory. Loops are detected between GS sub maps by place recognition and the relative pose between looped keyframes is optimized utilizing rendering and feature warping losses. After the global optimization of camera poses and Gaussian points, a structure refinement module enhances the reconstruction quality. With extensive evaluations on the EuRoc and KITTI datasets, LSG-SLAM achieves superior performance over existing Neural, 3DGS-based, and even traditional approaches. Project page: https://lsg-slam.github.io.

UAV-view geo-localization is crucial in many applications, such as material transportation and security inspection, particularly in GPS-denied urban environments. However, most existing methods assume a known drone flight altitude and divide satellite maps into tiles that approximate the scale of drone images, which are often inapplicable to real-world UAV scenarios where flight altitudes vary. In this paper, we propose a novel UAV-view geo-localization method, termed MM-Geo, to address the aforementioned issue. In particular, we partition the satellite imagery map into tiles of uniform size and retrieve the matching tiles in real time using online drone images of smaller field-of-view (FOV) at different altitudes. To address the multi-scale problem due to the varying altitudes, we design the patch vote rerank with match attention, and to tackle the multi-positive sample issue in the continuous, the normalized infoNCE loss is incorporated to provide finer supervision during contrastive learning. The proposed MM-Geo is extensively validated on the our own large-scale urban dataset MT-UAV as well as the public datasets UAV-VisLoc, outperforming the state-of-the-art (SOTA) approaches and achieving remarkable performance in practical drone delivery operations. To benefit the community, we will release the VisLoc-related code at: https://github.com/MM-Geo-2025/MM-Geo.

The acquisition of ground clutter data plays an important role in validating the feasibility of a spaceborne radar (SBR) system. Different from the airborne radar, the high production and maintenance costs of SBR systems make simulation a significant source for obtaining clutter data. However, high-precision clutter simulation typically demands significant computational resources and processing time. Moreover, the clutter range migration caused by high-speed motion of satellite platforms will make the clutter simulation and suppression problems more complicated. In this letter, we analyze the influence of clutter range migration on clutter-to-noise ratio (CNR) in an SBR system and conclude that whether or not the clutter range migration is considered does not affect the final CNR gain of the SBR system. The analytical results of this letter will significantly simplify the simulation process and provide a good reference for the engineering applications in the SBR system design.

This letter proposes a coherent integration approach for weak target detection with constant radial jerk in a low signal-to-noise (SNR) environment. In the proposed approach, the second-order Keystone transform (SoKT) is first employed to correct the quadratic range migration (QRM) induced by the radial acceleration of a target. Next, a velocity-jerk matched filter is constructed to eliminate the residual target radial velocity and jerk terms. Finally, the coherent integration is achieved through non-uniform fast Fourier transform (NuFFT) along the slow time. Compared with the conventional methods, the proposed approach can obtain comparable accumulation performance with a lower computational complexity. Simulation and real-measurement experiments are conducted to demonstrate the effectiveness and reliability of the proposed approach in low SNR environments.

In this letter, a new parameter estimation and multi-pulse coherent integration method for the high-speed and maneuvering target detection is proposed in a nonuniform sampling radar system. In the proposed method, the signal recovery with respect to the missing pulse data is firstly realized by using the iterative adaptive approach (IAA) in the range frequency domain. Then, the generalized nonuniform time-scaled transform (GNTST) is applied to decouple the complex coupling terms induced by the target maneuvering motion and irregular radar waveforms, where the velocity ambiguity caused by target high speed is considered. Finally, the well-focused target imaging result can be obtained after accomplishing the target signal recovery, reconstruction, and phase decoupling. Simulations and real-measured radar data experiments are conducted to validate the proposed method.

It is well-known that for a maneuvering target, its inverse synthetic aperture radar (ISAR) imaging quality may significantly deteriorate using the classical range-Doppler (RD) algorithm. To address this issue, this article proposes a novel ISAR imaging algorithm based on the generalized second-order time-scaled transform (GSOTST). In the proposed method, the multicomponent cubic phase signal (CPS) modeling is adopted for the radar echo signal after translational motion compensation (TMC) to portray the phase change characteristics more accurately. First, the target signal is transformed into the slow-time-delay-time domain using a correlation kernel function (CKF). Subsequently, the nonstationary phase is eliminated in the slow-time-generalized delay-time-frequency (GDTF) domain, and the GSOTST is used to decouple the temporal variables. Finally, the generalized Fourier transform is performed to transform the signal into the 2-D frequency domain, where the energy of the target signal is integrated into a well-focused 2-D peak, enabling the high-precision target parameter estimation and finely focused ISAR imaging. The experimental results from both simulation and real-measured data validate the effectiveness of the proposed algorithm.

Ground moving target focusing performance and processing efficiency are two important metrics in the synthetic aperture radar (SAR) system. Owing to the complex nonstationary phases caused by target motions, ground maneuvering targets are commonly smeared and distorted in an SAR image. To realize the ground maneuvering target imaging, exhaustive high-order parameter searching and optimization processing are usually needed to achieve the optimal processing performance. However, the large computational resource consumption may limit their real-time processing capabilities. To deal with this issue, an efficient SAR imaging approach for ground maneuvering targets is proposed by adopting the mixed symmetric function (MSF) transform. After performing the multichannel SAR clutter rejection and target initial detection by applying the constant false alarm rate (CFAR) technique, the linear envelop shift of detected ground target is eliminated by using the well-known Keystone transform (KT). Then, an MSF is constructed to rectify the range curvature and relieve the Doppler broadening after carrying out the nonuniform fast Fourier transform (NUFFT) corresponding to the second-order time variable. After removing the second-order Doppler spreading influence, the quadratic chirp parameter is estimated via the NUFFT after the symmetrical correlation processing. Finally, after accomplishing the motion compensation, the ground maneuvering target can be well focused and relocated in the SAR image. Compared with the traditional approaches, the proposed approach not only realizes the high-precision Doppler parameter estimation, but is also computationally efficient without grid searching procedure. Numerical simulation and real-measured SAR experiment results are depicted to verify the correctness and effectiveness of the proposed approach.

Due to existence of the long along-track baseline (ATB) among the different satellites in a distributed space-based radar (DSBR) system, a large number of grating lobes appear in radar returns, causing the non-continuous detection phenomenon of an air moving target (AMT). To solve this problem, in this paper, a novel approach for ATB distribution design in a DSBR system is proposed. In the proposed algorithm, to reduce the amount of spatial ambiguity points located at the main-lobe region and achieve the best air moving target indication (AMTI), the maximum target detectability ratio (TDR) in the main-lobe region is chosen to be the criteria for the optimal ATB design. The effectiveness of the proposed method is verified by the simulated multi-channel radar data in a DSBR system.

Employing azimuth multi-channel array configuration can effectively enhance the high-resolution and wide swath - synthetic aperture radar (HRWS-SAR) imaging performance by increasing the spatial sampling frequency along the azimuth direction. 1However, the ground moving targets with unknown radial velocities, may induce azimuth "ghost" points in a well-focused HRWS-SAR image with the imaging function related to ground static scene. To address this issue, the paper introduces a novel ground moving target imaging method in a multi-channel HRWS-SAR system. The multichannel HRWS-SAR simulation results verify the effectiveness and feasibility of the proposed method.

Mosaic mode synthetic aperture radar (SAR) combines the spotlight or sliding spotlight mode with a ScanSAR mode to accomplish the high azimuth resolution and large imaging swath SAR imaging. This paper studies the problem of the frequency modulated continuous wave (FMCW) SAR imaging with a Mosaic mode, where the imaging algorithms for pulse radars are not suitable for a FMCW SAR. To deal with this issue, this paper proposes a FMCW Mosaic mode SAR imaging algorithm based on modified polar formation algorithm (PFA), which considers the radar motion during the signal transmission and performs the SAR imaging operation for spotlight mode in each Mosaic unit. The simulation results of point targets verify the effectiveness and feasibility of the proposed method, which may provide a valuable reference for the application of Mosaic system to miniaturized platforms.

For a spaceborne bistatic early warning radar system with severe range dependence, the multi-channel division of the large-aperture antenna will bring about spatial under-sampling, which can lead to the spatial spectrum center estimation error and greatly influence the bistatic radar clutter suppression performance. To solve this problem, a novel bistatic clutter spectrum compensation method based on FFT range fitting is proposed in this paper. In the proposed method, the clutter Doppler and angle centers in different range gates are firstly estimated through Eigen decomposition. Then, the pseudo-single frequency complex sequence corresponding to the estimated spatial clutter center sequence is constructed, and the slope related to the angle centers is estimated after performing the FFT. Finally, the compensation matrix is constructed, so as to achieve the clutter non-stationary error compensation. The effectiveness of the proposed method is validated by the simulation experimental results.

Compared with a monostatic inverse synthetic aperture radar (ISAR) imaging system, a bistatic ISAR (Bi-ISAR) system offers more comprehensive target information, along with higher system security and resistance to interference. However, the inherent geometric configuration of Bi-ISAR will introduce the linear-geometry distortion (LGD), causing the target to appear sheared in shape and impacting subsequent target recognition. To deal with this issue, in this paper, a novel deep learning-based algorithm is proposed to realize the LGD correction. In the proposed algorithm, a neural network called DoubleUNet is employed for the accurate semantic segmentation of target's ISAR image. Then the initial target ISAR image is transformed into two-dimensional point clouds based on the estimated radar parameters. Finally, the linear coupling relationship between azimuth and range dimensions is removed, beneficial to the target ISAR shape restoration and subsequent recognition. Simulation experiments validate the proposed algorithm.

In this paper, a method aimed at removing the linearly continuous false alarm caused by the artificial building with unignored elevation in a multichannel synthetic aperture radar (SAR) system with ground moving target indication (GMTI) mode is proposed. In the proposed method, the Hough transform is firstly utilized to obtain the potential false alarms during the iteration procedure. After that, the linearly continuous false alarms are finally obtained and removed based on the phase similarity characteristic of those building scatters. Real-measured SAR data processing results are presented to verity the effectiveness and feasibility of the proposed method.

In this paper, the influence of different receiving array element weighting manners on the space-time adaptive processing (STAP) technique is analyzed. Firstly, the element-based echo signal in a space-borne early warning radar (SBEWR) system is established, and then the STAP procedure by utilizing two alternative receiving array element weighting models are introduced. After that, the antenna patterns with respect to two weighting manners are discussed and the target output signal-to-clutter-plus-noise ratio (SCNR) after STAP are evaluated. The numerical results show that the subarray element weighting manner obtains a better STAP performance without channel mismatch compensation while a slightly inferior STAP performance with channel mismatch compensation compared with the whole antenna weighting manner.

Abstract Synthetic aperture radar (SAR) image change detection suffers from poor quality of the difference image and low detection accuracy. Hence, this paper proposes a SAR image change detection method based on a fused difference image and an optimized random forest scheme, termed LRN‐SSARF. Specifically, a fusion operator difference image LRN is proposed, which is generated using a weighted fusion of log‐ratio (LR), ratio (R), and normalized ratio (NoR). This difference image generation method reduces noise's influence. Then, the Otsu algorithm is applied to segment the difference image and select the training samples. The training samples are input into the random forest (RF) model optimised by the sparrow search algorithm (SSA) for training and classification. Finally, the region link is uesd to refine the detection results and generate the final result. The change detection results of six real SAR image scenes highlight that the proposed algorithm has a high detection accuracy, and affords appealing integrity and detailed information about the change regions. Specially, the detection accuracy advantage of the Bangladesh dataset is larger, with the accuracy and Kappa coefficient reaching 98.04% and 92.00%, much higher than the competitor methods.

This letter proposes a novel airborne synthetic aperture radar (SAR) motion compensation (MOCO) method based on the optimal weighting Doppler frequency modulation rate estimation. According to the traditional map-drift (MD) algorithm, the Doppler frequency modulation rate corresponding to the range units can be estimated through azimuth sub-aperture signals. The utilization rate for range units and the contributions of the strong scattering units can be improved by using the proposed optimal weighting on the signal amplitude, increasing the estimation accuracy of the Doppler frequency modulation rate. The platform motion error can be effectively estimated through fitting the Doppler frequency modulation rate and each initial slant range based on least square processing. Real-measured high-resolution airborne SAR data validate the effectiveness and feasibility of the proposed algorithm.

This letter proposes a fast Doppler parameter estimation and refocusing method for ground moving targets in a synthetic aperture radar (SAR) system. In the proposed method, after implementing the main-lobe clutter rejection by using the azimuth adaptive processing technique, the range-azimuth positions of smeared target scatterers can be obtained via the constant false alarm rate (CFAR) detection. Then, an autocorrelation function is constructed to transform a moving target signal into the time-frequency plane, where the target parameters can be precisely and efficiently estimated by applying the scaled fast Fourier transform (FFT). Finally, ground moving targets can be well refocused and relocated in a SAR imagery. Compared with the conventional methods, the target output SNR can be enlarged about 3 dB under the low SNR by using the proposed parameter estimation method.

For maneuvering targets with weak surface reflections, the conventional ISAR imaging algorithms based on the motion parameter estimation for each scatterer may have a poor performance under low signal-to-noise ratio (SNR) scenario. To tackle this problem, a novel ISAR imaging algorithm is proposed in this letter. The effects of translational motion, the high-order migration through resolution cells (MTRC), and the nonstationary phase distribution are simultaneously eliminated by constructing the compensation function and utilizing the time polynomial rescaling-nonuniform fast Fourier transform (TPRS-NUFFT). Then, the particle swarm optimization (PSO) algorithm is applied to accomplish the motion parameter estimation. Finally, a high-resolution ISAR image can be obtained after motion compensation. Both simulation and real-measured data processing results demonstrate that the proposed algorithm can obtain focused ISAR image and improve the SNR by 29.8 dB.

A spaceborne bistatic surveillance radar system, which may increase the target radar cross section through a large bistatic angle, can effectively improve the performance of small air moving target indication. However, due to the separation of the transmitter and the receiver, the clutter exhibits severe range dependence in both Doppler and spatial-angle domains. This may significantly decrease the number of independent identically distributed samples and, thus, cause the degradation of clutter suppression performance. In addition, when an air moving target flies above the sea, the broadened spectrum of sea clutter caused by the random intrinsic motion will further influence the clutter suppression performance. To deal with these problems, this article establishes a multichannel sea clutter model for a spaceborne bistatic surveillance radar system. The calculation of the positions of the iso-range contours is analyzed first. Then, the bistatic sea clutter model is established based on the directional wave spectrum, the monostatic–bistatic equivalence theorem, and the two-scale scattering model, where the Earth rotation and the range ambiguity of clutter are considered. Finally, the range dependence and the spectrum characteristics of bistatic sea clutter are discussed, and the clutter suppression performances under different azimuth bistatic angles, different elevation angles, and different sea states are analyzed. In addition, the influence of clutter range ambiguity is analyzed based on the simulations.

To achieve tight beam coverage and obtain the optimal target detection performance for a space-based early warning radar (SBEWR) system, the reasonable radar beam design is prerequisite. In this paper, an optimal beam position design algorithm in a SBEWR system is proposed, where the beam position overlap rate (BPOR) choice is reasonably designed. In the proposed algorithm, the maximum signal-to-clutter and noise ratio (SCNR) of the targets located at the beam position edge is chosen as the criterion for the BPOR optimization design. The BPOR designment result provides important guidance and references for beam filling designment and guarantee the effective target detection capability of a SBEWR system.

Due to existence of the long along-track baseline (ATB) between the satellites in a distributed space-based early warning radar (DSBEWR) system, a large number of grating lobes appear in radar returns, causing the noncontinuous detection phenomenon of an aerial moving target (AMT). To solve this problem, in this paper, a novel approach for ATB distribution design in a muti-satellite DSBEWR system is proposed. In the proposed algorithm, to reduce the amount of spatial ambiguity points located at the main-lobe region, the main-lobe gain of the received antenna pattern in the azimuth dimension is designed to be maximum via the non-uniformly intersatellite ATB designment. The effectiveness of the proposed method is verified by the simulated multi-channel radar data in a DSBEWR system.

As one of the main statistical characteristics of clutter, the clutter amplitude distribution plays an important role in the accurate modeling of spaceborne multi-channel radar signal as well as the subsequent, maritime radar target detection. In this paper, considering that the space-time adaptive processing (STAP) technology is usually applied to accomplish the main-lobe clutter rejection in a space-borne surveillance radar, the influence of different clutter amplitude distributions on STAP in a spaceborne multichannel radar system are analyzed. Firstly, a spaceborne multi-channel clutter model is established based on radar equation and clutter space-time steering vector. Then, Rayleigh distribution, Weibull distribution, lognormal distribution, K distribution, generalized Pareto distribution, and IG-CG distribution are used to fit the clutter amplitude. Finally, the effects of these clutter amplitude distributions on STAP performance are analyzed, respectively. The simulation results show that in the case of the same clutter power, the influence of different clutter amplitude distributions on STAP performance is approximately the same.

The space-based surveillance radar system has a higher field of view and can overcome interference from Earth's atmosphere and terrain occlusion, which has been widely applied in high-threat near-Earth asteroid (NEA) warning and defense applications. Due to the limited power aperture product of the space-based system and the far distance between the radar and asteroid targets, the target signal is extremely weak. Prolonging the coherent accumulation time can effectively improve the radar detection capability of small asteroid targets, but the complex effects of range migration (RM) and Doppler frequency migration (DFM) will degrade the target coherent accumulation performance. To effectively solve this problem, an improved Keystone transform (KT) matched filtering banks method based on particle swarm optimization algorithm is proposed. Compared with traditional methods, the proposed method can not only ensure that the asteroid target detection performance is close to the theoretical optimum, but also reduce the system computation complexity. Simulation results verify the effectiveness of the proposed algorithm.

In this letter, a high-resolution SAR imaging algorithm based on a modified omega-K algorithm is proposed. The proposed algorithm first multiplies the reference signal. Then, the azimuth non-stationary phase error (ANSPE) is considered after performing the interpolation process based on the generalized frequency scale transformation. Finally, a well-focused SAR image can be obtained after compensating the ANSPE and correcting the residual spatial variant range migration by utilizing the range segmentation technique. The simulation results of point targets and the real-measured airborne high-resolution SAR data simultaneously verify the effectiveness and feasibility of the proposed algorithm.

In this letter, the clutter range dependence issue caused by crab angle is investigated in an airborne multi-channel radar system. A new method is proposed to accomplish the crab angle estimation and the non-stationary error compensation. Firstly, from the center-biased and spectrum-broadened characteristics of the clutter space-time spectrum in the case of non-neglected crab angle, a 1-D cost function based on space-time spectrum broadening degree is constructed to achieve the accurate crab angle estimation. Then, the clutter non-stationary errors can be effectively compensated in the post-Doppler domain, significantly improving the subsequent space time adaptive processing (STAP) performance. Simulation results are presented to validate the feasibility and effectiveness of the proposed method.

For air moving target indication in nadir region, due to the fact that a spaceborne radar beam can illuminate the top of fuselage, the target radar cross section is usually high, which is beneficial for the detection of a low-observable target. However, due to the short slant range, specular reflection effect, and relatively low radar ground resolution, the power of clutter component from nadir region is comparatively high, leading to the insufficient clutter suppression and the degradation of target detection performance. Fortunately, when an air moving target is adequately high, the target echo is able to be separated from the main clutter echoes due to a shorter time delay, making it possible to be only mixed with low-power ambiguous clutter echoes. Based on these considerations, this paper analyzes the performance of air moving target indication in nadir region for a spaceborne surveillance radar system. It analyzes the target minimum detectable velocities with different target heights and beam center elevation angles. Also, an effective sample selection method based on adaptive range segmentation is proposed to solve the power heterogeneity issue between the main clutter area and the range ambiguous clutter area. As a conclusion, the larger the elevation angle of an air moving target is, the higher the minimum target detectable height is.

Synthetic aperture radar (SAR) is an advanced remote sensing technique, capable of observing Earth’s surface independent of weather conditions and sunlight illumination. Restricted by the minimum antenna area, however, conventional spaceborne SAR systems cannot achieve high azimuth resolution in a wide swath. In addition, blind ranges are present as the constant pulse repetition interval (PRI) is used. To solve these problems, a PRI-staggered elevation multichannel SAR (EMC-SAR) system is employed in this article. By transmitting the continuously PRI-varied sequence, the blind ranges are located at different regions in different receive instants, effectively avoiding the loss of coverage in elevation. In this system, three issues are required to be addressed: 1) recovering the missed data located at blind ranges; 2) suppressing range ambiguous components; and 3) restoring the PRI-varied signal into a regular grid. To deal with these problems, we propose a novel SAR imaging method for a PRI-staggered EMC-SAR system. To be applied on-ground, assume downlinking of the individual elevation channels. First, the modified$\varepsilon $-insensitive loss tube regression with the L2 regularization method is applied to recover the missed data. Then, the range ambiguous components are suppressed by performing digital beamforming (DBF) based on the elevation multichannel technique, where the covariance matrix is constructed by using an iterative adaptive algorithm. After that, a generalized scaling transform is employed to restore the PRI-varied signal into a uniform sampled grid. Finally, a well-focused SAR image can be obtained by performing the conventional SAR imaging techniques. The effectiveness of the proposed method is validated by both simulated and real SAR data processing results.

Multiple-input–multiple-output (MIMO) technique has recently received great attention due to its promising prospect for ground moving target indication (GMTI) applications in a synthetic aperture radar (SAR) system. In this article, an SAR-GMTI processing technique in an MIMO-SAR system with dual-channel configuration is proposed. In the proposed method, up and down chirps are adopted as the transmitted waveforms. Then, the baseline estimation and clutter rejection are simultaneously accomplished in the raw Doppler data domain, after which two separated range-compressed echo signals corresponding to two transmitters can be obtained without the influence of cross-correlated interferences of the clutters. Subsequently, a ground moving target can be detected, clustered, finely refocused based on the Doppler chirp parameter estimation, and accurately relocated by using the along-track interferometry (ATI) processing technique. Simulated experiments are implemented to validate and evaluate the feasibility of the proposed method.

Due to the existence of the time synchronization error, the correlation coefficient between the main satellite and auxiliary satellite severely declines, thus deteriorating the clutter suppression ability and aerial moving target indication (AMTI) performance in a distributed space-based early warning radar (DSBEWR) system. In this paper, a novel algorithm is proposed to estimate and calibrate the linear time synchronization error. In the proposed algorithm, the rough linear time synchronization error range is firstly estimated according to the Doppler offset between the same spatial channel in the main satellite and the auxiliary satellite. After obtaining the searched rough error range, the linear time synchronization error can be accurately obtained by finding the max correlation coefficient between the main satellite and auxiliary satellite after performing time synchronization error compensation. The effectiveness of the proposed method is verified by the simulated multi-channel radar data in a DSBEWR system.

Due to existence of the long along-track baseline (ATB) in a distributed space-based early warning radar (DSBEWR) system, a large number of grating lobes appears in both main-lobe and side-lobe clutter regions, causing the discontinuously detectable phenomenon of an aerial moving target (AMT). To solve this problem, in this paper, a novel spatial ambiguity suppression algorithm is proposed. In the proposed algorithm, based on the prior information provided by the system parameters and the signal processing results from the single satellite radar data, after performing the multi-channel clutter rejection, the spatial ambiguity can be eliminated via the joint processing of residual clutter signals corresponding to the single satellite and the muti-satellites. Simulated processing results are provided to validate the effectiveness of the proposed method.

Geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO-BiSAR) has the advantages of wide beam coverage, long exposure time, and superior system flexibility. However, it is a challenge for GEO-BiSAR to efficiently acquire SAR images both with high resolution and wide swath due to the severe range and azimuth spatial variances. To deal with this issue, a modified Omega-K method is proposed in this paper. In the proposed algorithm, an equivalent range model associated with GEO-BiSAR configuration is built, where the equivalent parameters of the airborne radar receiver absorb the phase parameters corresponding to the GEO transmitter. Then, the Stolt interpolation is performed to realize the range-azimuth decoupling, achieving the linearization between range and frequency variables. Finally, a well-focused SAR image could be obtained after residual spatial variance error compensation. Simulations are presented to demonstrate the effectiveness of the proposed method.

By transmitting the constant pulse repetition interval (PRI) sequence, the traditional synthetic aperture radar (SAR) system will suffer from the loss of coverage in elevation. Thus a staggered-SAR system is developed in recent years, which employs the continuously PRI-varied sequence, making the blind ranges locate at different regions in different receive instant. However, the nonuniformly sampled signal will cause ambiguities in the SAR imaging result. To deal with this issue, in this paper, a novel signal restoration method based on kernel regression is proposed to resample the nonuniform signal. Real-measured spaceborne SAR data is used to validate the proposed method.

Next-generation SAR imaging system demands wide-swath and high resolution to observe Earth's surface, promoting the development of the high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system working in terrain observation by progressive scans (TOPS). However, Doppler ambiguities will occur in this system, causing the imaging performance severely degrading. To address this issue, in this paper, a novel Doppler ambiguous suppression method for an HRWS-TOPS SAR system based on the orthogonal projection subspace is developed. The effectiveness of the proposed method is verified by both the simulated and real SAR data.

The marine moving target detection is a very attractive research field in recent years. However, due to the existence of sea clutter with complex internal motion, the traditional space-time adaptive processing (STAP) methods may not obtain good processing performance. In this paper, the multichannel sea clutter is modeled and the time-varying and space-varying properties of sea clutter are analyzed. In addition, the time-frequency distribution characteristics of sea clutter are analyzed. Finally, a multichannel sea clutter suppression method based on short-time Fourier transform is proposed. The simulation results show that the proposed method can achieve better sea clutter suppression performance than that of the traditional STAP method.

In this letter, a novel method is proposed to suppress the terrain fluctuation clutter based on adaptive broadening processing. In the proposed algorithm, the flat interference phase is first compensated according to the priori radar system parameters. Then, according to the space-time trajectory distribution of clutter edge, the level of clutter Doppler spread in virtue of crab effect is estimated by a cost function related to the clutter eigenvector matrix. Finally, after calculating the clutter suppression weight vector according to the broadened clutter subspace, the non-stationary ground clutter can be robustly rejected. The validity of the proposed method is verified by both the simulated and real-measured multichannel radar data.

The spaceborne high-resolution wide-swath synthetic aperture radar (HRWS-SAR) system generally does not meet the optimal SAR imaging configuration, and thus, it is necessary to apply digital beam-forming filtering technology to restore the nonuniformly sampled signal into the uniform grids. However, in practice, because of the influences of temperature, receiving machine, and other error factors, the channel errors may possibly exist, causing the SAR image to be smeared. To address this issue, this letter proposes a novel algorithm based on the hybrid artificial fish school algorithm–glowworm swarm optimization–genetic algorithm (AFSA-GSO-GA) to address the channel imbalance issue. First, coarse HRWS-SAR imaging processing is performed to obtain the positions of the Doppler ambiguity components. Then, according to the designed cost function, the hybrid AFSA-GSO-GA algorithm is used to realize the channel phase error estimation. Finally, a well-focused SAR image can be obtained after channel balance. The effectiveness of the proposed method is validated by both simulated and real SAR data.

The rapid development of the modern electronic counter-countermeasures (ECCMs) has made the conventional inverse synthetic aperture radar (ISAR) associated with the regular signal waveforms more vulnerable and unreliable. To deal with this issue, the complex waveform designment with staggered pulse sampling is developed in an ISAR system in this letter. In the proposed method, the generalized time-scaled transform (GTST) is adopted to effectively accomplish the irregular signal reconstruction and linear phase decoupling. After that, a set of matched filtering functions are established and interspersed in the imaging processes to accomplish the subsequent motion compensation, target imaging, and range and cross-range scaling. Finally, a satisfied ISAR imagery corresponding to the real size of a moving target can be recovered. The simulation and real-measured radar data processing results are applied to demonstrate the effectiveness of the proposed method.

Sea clutter magnitude distribution exhibits important guiding significance for the design of marine target detection algorithms and the selection of the constant false alarm rate (CFAR) detection threshold. In this letter, to deal with the limitation of the traditional generalized-K (GK) distribution in highly heterogeneous clutter scene, a modified GK (MGK) distribution is proposed for sea clutter magnitude. By combining the traditional GK and generalized Pareto distributions, the value range of power parameter and the applicable range of the distribution model are extended. The applicability of the proposed distribution model is verified by real-measured sea clutter data.

As for a new-style radar, the random pulse repetition interval (PRI) pulse waveform may be transmitted, and then the target Doppler parameter estimation precision and detection ability by using the conventional techniques may be invalid due to the irregular signal sampling. To solve these problems, this letter proposes a simple, yet effective and quasi-optimal coherent integration method with random pulse resampling. The proposed method first reconstructs the nonuniformly sampled signal into a uniform grid in the range frequency domain based on the modified sinc interpolation theory. Then, a matched filtering function related to the target chirp parameter is constructed in order to eliminate the residual coupling between range and azimuth. Finally, a moving target can be well imaged after performing the target motion compensation.

In this letter, we propose a novel range and Doppler sampling mismatch compensation method for moving target detection, which can effectively improve the output signal-to-noise ratio (SNR) of a moving target. In the proposed method, after performing the target coherent integration by using the well-known Keystone transform (KT), the range and Doppler sampling mismatch errors (SMEs) are estimated and compensated based on the constructed optimization model with the consideration of the change rate of a moving target peak amplitude. In order to improve the computational efficiency, the Hooke–Jeeves method is applied to achieve the optimal solution of the constructed optimization problem, thus efficiently solving the target energy diffusion problem caused by the SMEs. Simulated experiment is presented to verify the effectiveness and feasibility of the proposed method.

In this paper, we propose a novel long-time coherent integration detection method to detect an uncooperative moving target in a frequency and pulse repetition interval randomly jittering radar system based on signum coding (SC). In the proposed algorithm, an additional reference waveform is applied to eliminate the third-order harmonic influence induced by SC. Then, a generalized Keystone transform (GKT) is proposed to resolve the complex coupling among the range frequency, jittered carrier frequency, and nonuniformly sampled time. Simulation results are presented to validate the effectiveness and feasibility of the proposed method.

Due to the limited size, carrying capacity, power-aperture product, and high hardware cost of satellite platform, the traditional single-platform spaceborne radar system encounters the problems of poor target minimum detectable velocity (MDV) performance, considerably deteriorating the moving target detection performance. To improve the air moving target indication (AMTI) performance, especially for a weak target, distributed space-based radar system (DSBR) becomes a good candidate due to the longer along-track baseline (ATB) and spatial power synthesis. However, due to the sparse configuration of radar baseline distribution, the detection performance of air moving targets (AMTs) will be restricted by many practical factors in an actual DSBR system. In this paper, multi-channel signal models of an observed moving target and ground clutter are accurately established in a DSBR framework, where the error influences of cross-track baseline (CTB), terrain fluctuation, and channel inconsistency response are considered. Then, the influence of the non-ideal factors, including the channel noise, long-intersatellite ATB, long-intersatellite CTB, synchronization errors, and interchannel amplitude and phase inconsistency errors, on the AMTI performance is analyzed term by term. The simulation results provide the useful guidance for the system design of a DSBR with the AMTI tasks.

In the inshore region, the azimuth ambiguity of land clutter is the main factor, which reduces the clutter suppression performance for the multichannel synthetic aperture radar (SAR) system. To address this problem, we propose a multifeature tensor discriminant alternating optimization and affine invariant Riemannian (TDAO-AIR) classification approach. First, the correlation coefficient between channels is obtained to characterize the normalized SAR image amplitude. Next, multilook interferometric processing is performed to reduce the effect of Speckle noise and acquire multilook interferometric complex images. At the same time, we extract the correlation coefficient gradient and the multilook interferometric phase gradient in different spatial directions to characterize the spatial structure. Next, we construct a feature tensor (FT), including correlation coefficient between channels, multilook interferometric phase, and spatial structural gradient information for each range-Doppler unit (RDU). The TDAO algorithm is proposed to extract the core FT (CFT) by optimizing the objective function and getting the projection matrix. Then, the CFT is unfolded along the feature dimension, and the feature covariance matrix (FCM) is constructed for each RDU. With the AIR distance measure, the inshore region is divided into different clutter regions. Finally, in the azimuth ambiguous clutter region, independent and identically distributed (IID) training samples are selected to estimate the clutter covariance matrix (CCM), and robust adaptive clutter suppression is performed in the SAR image domain. The experiments on simulated and real measured data by TerraSAR-X demonstrate that the proposed method can accurately identify and suppress the azimuth ambiguity in the inshore region.

For a spaceborne high-resolution and wide-swath synthetic aperture radar (HRWS-SAR) system, it usually uses the digital beamforming technology. However, in practice, because of the influences of temperature, antenna pattern, receiving antenna, and other error factors, there may exist the range synchronization time errors, amplitude errors, and phase errors among different spatial channels. These nonideal factors will significantly degrade the multichannel data reconstruction performance, resulting in a smeared SAR image. To address this issue, in this article we propose a novel channel error correction algorithm based on the orthogonal projection theory. First, the optimal weight of each Doppler ambiguity component is calculated by the orthogonal projection. Then, the cost function is constructed based on the power maximization criterion, from which the channel phase errors can be obtained. Finally, the HRWS-SAR imaging can be finely realized after performing the channel balancing. Compared with the conventional phase error estimation method, the proposed algorithm does not require to perform the matrix eigenvalue decomposition, avoiding the signal leakage phenomenon under low SNR case. The effectiveness of the proposed algorithm is validated by both airborne and space-borne real SAR data.

In a multichannel synthetic aperture radar (SAR) system, because of the target uncooperative motion, a ground moving target (GMT) is usually smeared, distorted, and shifted in an SAR image. In this article, a novel approach for multichannel SAR-GMT indication (GMTI) processing is proposed. The main innovations of this method are that a GMT can be well refocused and relocated since the target high-order Doppler parameters can be precisely estimated based on a high-order polynomial phase signal (PPS) model, and the target statistical amplitude and phase information is jointly applied to improve the radial velocity estimation robustness. Compared with the current SAR-GMTI algorithms, the improvements of this method over the existing methods are: 1) the topography interferometric phase can be effectively compensated by applying an adaptive 2-D spectrum filtering technique via iterative processing; 2) a GMT can be well imaged since the target Doppler chirp rate and the quadratic chirp rate can be well estimated via the 2-D coherent integration in the time–frequency plane; and 3) a GMT can be precisely relocated into its original position by applying the generalized amplitude and phase weighting technique. Real-measured SAR data processing results are presented to validate the effectiveness and feasibility of the proposed method.

In inverse synthetic aperture radar (ISAR) imaging for a uniformly moving rigid-body target, a finely focused ISAR image can be obtained by using the conventional range-Doppler algorithm. However, the ISAR image quality may significantly deteriorate when the time-vary Doppler phases in virtue of target maneuvering motions are present, such as an airplane with nonuniformly rotation and a ship with fluctuation. This has become a challenging task, especially under nonhigh signal-to-noise ratio (SNR) environment. In this article, a novel ISAR imaging algorithm for a maneuvering target with moderate reflection intensity is proposed. After motion compensation, the radar echo signal in a range cell is modeled as a multicomponent cubic phase signal (CPS), in which the chirp rate and the quadratic chirp rate are two important physical quantities that may determine the target ISAR focusing quality. Based on a symmetrical instantaneous autocorrelation function, the received CPSs are transformed into the time and lag-time plane, and then a 2-D coherent integration can be realized after the generalized time-scaled transform and 1-D maximization. This forms a high-quality ISAR image. The effectiveness and superiority of the proposed algorithm are validated by the ISAR imaging results of simulated and real measured data.

Due to the high-speed movement of a spaceborne radar (SBR) platform, the geographic clutter spectrum expands severely, resulting in the useful moving target signal submerged by the main-lobe clutter background. To deal with this issue, the equipped multichannel arrays in an SBR system provide sufficient spatial degrees, and as a consequence, the space-time adaptive processing (STAP) technology is often preferred to achieve the moving target detection, even in the main-lobe clutter regions. However, for the moving target detection under the sea scene, due to the complex internal motion of sea clutter, the clutter signal received by an SBR system may possess the space- and time-varying characteristics, worsening the multichannel clutter rejection performance using the traditional STAP techniques. In this article, a novel sea clutter suppression method based on the joint space-time-frequency adaptive filtering is proposed. In the proposed algorithm, according to the coherent time analysis of sea clutter, the subaperture time-domain sliding window is employed to alleviate the clutter decorrelation effect, and then, a modified subspace projection technique is applied to accomplish the first-stage clutter rejection. After realizing the effective signal recovery with respect to these residual subaperture clutter data, the second-stage spatial filtering method is applied to realize the final clutter suppression with respect to the relatively high Doppler resolution clutter returns. The effectiveness of the proposed algorithm is verified by both simulated multichannel sea clutter data and real-measured sea clutter data.

When an early warning radar installed in a spaceborne platform works in a down-looking mode to detect a low-altitude flying target, the severely broadened main-lobe clutter cannot be ignored, which will cause the deterioration of the moving target detection capability. To deal with this problem, a space–time adaptive processing (STAP) technique is proposed for effective clutter suppression based on the spatial–temporal 2-D joint filtering. However, the full-dimensional optimal STAP encounters the challenges of high computational complexity and large training sample requirement. Therefore, the dimension-reduced STAP technique becomes necessary. This article proposes a novel dimension-reduced STAP algorithm based on spatial–temporal 2-D sliding window processing. First, several sets of spatial–temporal data are obtained by using spatial–temporal 2-D sliding window. Then, for each set of data, the 2-D discrete Fourier transform is performed to transform the echo data into the angle-Doppler domain. Finally, jointly adaptive processing is performed to realize the clutter suppression. Compared with the conventional STAP algorithms, the improvements of this method over the existing methods are: 1) the proposed method requires fewer training samples due to the 2-D localization processing and 2) the proposed method can obtain the better clutter suppression performance with lower computational complexity. The feasibility and effectiveness of the proposed algorithm are verified by both simulated and real-measured multichannel surveillance radar data.

Synthetic aperture radar (SAR) target recognition is essential for SAR image interpretation. It has been widely used in national defense and national economy. At present, the SAR image detection and recognition methods based on convolutional neural network (CNN) have problems such as insufficient extraction of the feature information of SAR image targets, false targets caused by the interference of complex backgrounds, and low detection performance. The main reason is that the feature extraction of CNN is a local operation in space and time, which ignores the correlation between pixels and regions and the dependencies between channels in SAR images. In this paper, a non-local channel attention network (NLCANet) SAR image target recognition method is proposed based on the GoogLeNet structure combined with asymmetric pyramid non-local block (APNB) and squeeze-and-excitation block (SEB). APNB is added to the GoogLeNet framework to capture more context information and enhance the correlation between pixels and regions. SEB is added to the Inception structure to become Inception-SEB (ISEB), through which channel dependencies based on the fusion of different scale features can be obtained. The experimental results based on the moving and stationary target acquisition and recognition (MSTAR) dataset and the SAR ship detection dataset (SSDD) show that the proposed method improves the detection ability of targets in complex backgrounds and achieves better land-sea target recognition performance.

High speed, high maneuverability, and weak space-moving targets (SMTs) are major threats for space-borne radar (SBR) systems. First, the severe range migration (RM) and Doppler extension are induced by complex relative motion between the radar platform and non-cooperative moving targets, making moving target detection (MTD), and parameter estimation particularly difficult. Apart from this challenge, Doppler ambiguity and Doppler aliasing arise from the limited pulse repetition frequency (PRF) of a SBR system to ensure an adequate coverage rate, which may make the existing MTD algorithms deteriorate dramatically. To address these issues, we focus on the detection of high speed and high maneuverability targets based on the modified keystone transform matched filtering (MKTMF), whose Doppler frequency exceeds PRF as well as spans multiple PRFs. The proposed method is suitable for the weak MTD under a low signal-to-noise ratio (SNR) case since the nonlinear operation is not involved. Finally, some numerical results and real data results are provided to validate the superiority of the proposed method.

In this paper, a precise beam position design algorithm for space-based early warning radar (SEWR) based on the actual antenna pattern search is proposed. In the proposed algorithm, the beam positions along elevation and azimuth dimensions are filled according to the main lobe widths of elevation and azimuth antenna patterns, respectively, and thus the beam position designment is more accurate since the range and azimuth angle dependences are considered. Based on the designed beam positions, the precise dwell time can be obtained according to the coverage rata of a SEWR system. The beam position designment results by using the proposed method provide a reference for the beam filling designment of a space-based radar.

The azimuth multichannel spaceborne SAR system can achieve the high-resolution and wide-swath simultaneously. In a high-resolution and wide swath (HRWS) SAR system, the Doppler centroid (DC) frequency estimation is an important step for Doppler ambiguity signal reconstruction, which should be precisely estimated. In this paper, we propose a novel Doppler centroid frequency estimation method for HRWS-SAR system based on modified shuffled frog leafing algorithm (MSFLA). Firstly, the cost function is designed based on the average power difference between ambiguities and useful signal. Then, the MLSFA algorithm is proposed to accomplish the DC estimation. Finally, a good HRWS-SAR image is obtained based on the estimated DC. Compared with the traditional time-domain based DC estimation methods, the proposed method is not affected by the troubled cross-terms induced by high-order moments. The effectiveness of the proposed method is verified by the simulation experiments and real-measured SAR data.

Narrow-band interference (NBI) is a major threat for synthetic aperture radar (SAR) system, which degrades the imaging quality severely. In this paper, a new type of narrow-band interference (NBI) is investigated, i.e., periodicity transmitted NBI (PT -NBI). Compared with the conventional NBI, PT -NBI is intermittently emerged in one azimuth pulse, which shows periodic property in the range time domain and broadband characteristic in the range frequency domain, resulting in the severe performance degradation by using the existing NBI suppression methods. The distinctions and characteristics between the NBI and PT -NBI in different transform domains are analyzed firstly. On this basis, a PT -NBI suppression method is proposed by applying the joint time domain detection and Eigensubspace (ESP) filtering in this paper, which can effectively realize the interference suppression for PT - NBI.

This letter deals with the motion parameter estimation and focusing for ground moving targets in synthetic aperture radar (SAR) imagery. In the proposed algorithm, after range compression, the echo signal of a moving target is first transformed into the range-frequency and Doppler domain. Then, the target signal is characterized as an inclined trajectory after applying the correlation operation with respect to the divided Doppler subaperture data. Finally, target motion parameter estimation and focusing can be effectively accomplished based on the Doppler axis rotation transform (DART). The effectiveness of the proposed algorithm is validated by both simulated and real airborne/spaceborne SAR data.

In this letter, a novel method is proposed to estimate the along-track baseline for a multichannel synthetic aperture radar (SAR) system, intended for ground moving target indication (GMTI) applications. First, an adaptive spectrum filtering technique is proposed to compensate the terrain interferometric phase caused by the cross-track baseline and then the ground clutter is rejected by applying the joint-pixel displaced phase center antenna (JPDPCA). After performing the moving target detection, the target radial velocity is estimated according to the azimuth position offset by exploiting the road-aided information. Finally, the along-track baseline is derived based on the subspace projection (SP). The effectiveness of the proposed method is validated by data from the experimental airborne system.

In this article, we propose a multichannel sea clutter model in a spaceborne early warning radar system and analyze the influence of the sea clutter motion characteristics on the space-time adaptive processing (STAP) performance. To establish a multichannel sea clutter model, the 3-D Gerstner wave model is applied to construct the sea surface. Then the Pierson–Moskowitz wave spectrum and the stereo wave observation project (SWOP) directional spectrum are combined to describe the amplitude distribution of waves in different frequencies and directions. At the same time, the two-scale model is applied to obtain the specific backscattering coefficients of sea clutter at different time and positions. In addition, breaking waves are added in sea clutter returns with the form of false targets. Finally, the space-time distribution characteristics of sea clutter in a spaceborne multichannel array system and the influences of sea clutter under different wind speeds and directions on STAP performance are analyzed based on the simulation processing results. Processing results of some real-measured radar data are also exhibited to verify the theoretical analyses.

This letter addresses the long-time coherent integration issue for weak maneuvering target detection in a staggered pulse repetition interval (PRI) radar system. Due to the random phase fluctuation caused by the varied PRI, the complex range-azimuth coupling effects will significantly deteriorate the maneuvering target detection performance. In this letter, the nonuniformly sampled signal is efficiently reconstructed into a uniform grid based on the filterbank framework, where the uniformly sampled spectrum of a fast-moving target with Doppler ambiguity can also be reconstructed. After compensating the coupling influence caused by target motion, a moving target can be finely focused. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

The purpose of this letter is to solve the space-time off-grid problem caused by the extremely inhomogeneous clutter spectrum under the space/airborne bistatic system. For the estimation of inhomogeneous clutter spectrum, the method of sparse recovery (SR) is usually adopted. However, performance of sparse recovery may degrade since the real clutter point often does not fall in the center of the space-time grid. To deal with this issue, a local mesh splitting algorithm is proposed in this letter. First, select the space-time vector atoms which are most relevant to the residuals by subspace projection. Then, iterative local mesh splitting is performed around the selected atoms to match the real clutter points. Finally, the clutter subspace is reconstructed from all matched atoms selected by global and local iterations. The simulation results show that the method can obtain better accuracy with lower computational effort.

Synthetic aperture radar (SAR) operating in an along-track interferometric (ATI) mode has the advantage of minimum detectable velocity (MDV) in active marine target detection. However, most of the conventional ATI detectors fail to identify the marine targets cruising with blind speeds, in which case the interferometric phases of these targets are closely distributed around those of the sea background due to phase wrapping and aliasing through ATI processing. To address this issue, we propose a method to detect the active marine targets based on complex interferometric dissimilarity for dual-channel ATI-SAR systems. First, an interferometric bilateral filter is designed to smooth the random noises and locally adapt to the spatial structure of the interferogram for measuring the interferometric magnitude and phase. Then, the target detection metric is constructed based on the complex interferometric dissimilarity between the marine targets and the sea background. By adaptively regressing into a magnitude-based test toward the blind-speed targets, the target detection metric can mitigate the blind-speed detection problem and thus yield a satisfactory detection result. Furthermore, this metric is of a constant false-alarm rate (CFAR), and its probability density function (pdf) is derived to facilitate the detection threshold computation. Finally, both the simulated and real-data processing results are given to validate the superiorities of the proposed method.

Much research from open literature shows that polarization diversity can provide another dimension which may be exploited to improve the performance in ground moving target indication (GMTI), compared with space-time adaptive processing (STAP). In this article, we report the multichannel synthetic aperture radar (SAR)-based GMTI (SAR-GMTI) experiment and its preliminary results with a N-SAR system which is an airborne quadrature-polarimetric (quad-pol) radar system. First, the joint polarization-space adaptive processing (JPolSAP) is performed in the image level, but two suboptimal versions of JPolSAP, where the polarimetric matched filter (PMF) vector and the full-one vector are exploited to substitute for the polarimetric steering vector, respectively, are evaluated since the polarimetric steering vector of the moving target is unknown precisely in practical applications. Then, considering the computational complexity and lack of secondary data in a inhomogeneous environment due to high degrees of freedom of the JPolSAP processor, two cascade processors are evaluated, including the polarization enhancement that uses PMF and noncoherence integration detection (NCID) technique. Furthermore, we utilize the polarization information to accomplish SAR terrain classification, and subsequently secondary data from the same scattering type clutter can be obtained for clutter suppression under the guidance of polarization classification results as a priori knowledge. Finally, the experimental results demonstrate that: 1) the suboptimal JPolSAP processor with PMF steering vector can effectively enhance GMTI performance about 13 dB (or even up to 5 dB) relative to the worst (or best) single-polarization (S-pol) processor case and has the best robustness compared with the one with full-one steering vector; 2) polarization enhancement using PMF also obtains a good output gain of polarization filter, especially for quad-pol enhancement, which gains up to 2-3 dB with respect to the best output of S-pol processor, and the NCID technique can obtain good performance of moving-target detection; and 3) under the guidance of polarization classification results, the capability of clutter suppression can improve even up to 15 dB with respect to the one without classification.

Azimuth superresolution for real beam scanning radar aims to recover the high-resolution image from low-resolution echo. Among superresolution techniques, regularization-based methods are widely used, but most existing methods lead to the blurring of scattering targets and thus are difficult to distinguish between close targets. In this paper, we propose to employ the nonconvex ℓp-regularization with 0 <; p <; 1 to achieve the sparsity-driven superresolution, which further enhances the azimuth resolution. Furthermore, the resultant optimization problem is efficiently solved using an unified framework via incorporating different proximity operators. Simulation results validate the accuracy and efficiency of the proposed algorithm.

This letter addresses the coherent integration problem for the moving target detection in a frequency agile radar system. Due to the random phase fluctuation caused by the carrier frequency random hopping, the complex range-azimuth coupling effects will significantly deteriorate the target integration performance. In this letter, echoes are classified into different bursts according to the carrier frequencies, and then keystone transform (KT) is applied to correct range walk in every burst. After compensating the range offsets among different bursts and rearranging the signal returns, the scaled transform is constructed to remove the residual coupling between the agile carrier frequency and slow-time. Finally, a moving target can be well-focused in the frequency-velocity domain. Simulated results are provided to validate the effectiveness of the proposed algorithm.

In this letter, a novel method is proposed to estimate the along-track baseline for a multichannel high-resolution and wide-swath (HRSW) synthetic aperture radar (SAR) system. First, a spatial correlation function is constructed to remove the range cell migration and Doppler broadening of ground static targets. Then, the iterative adaptive approach (IAA) is applied to iteratively estimate the along-track baseline with high precision. Finally, a high-resolution SAR image can be obtained based on the estimated baseline. The effectiveness of the proposed algorithm is validated by both simulated and real SAR data.

In this paper, a new algorithm is presented to image ground moving targets in a synthetic aperture radar (SAR) system based on range frequency reversal transform-fractional Fourier transform (RFRT-FrFT). In this algorithm, a range compressed signal is initially transformed into the range frequency domain and then RFRT is proposed to directly compensate the range migration via multiplying the signal in the range frequency domain by its reversed data according to the equal interval sampling of range frequency variable, which can significantly decrease the computational complexity in target envelope migration elimination. Then, FrFT is applied to accomplish the target motion parameter estimation after range migration alignment. Finally, a ground moving target is well focused after motion compensation. The effectiveness of the proposed algorithm is validated by both simulated and real SAR data.

In this paper, we propose a long-time coherent integration method for a maneuvering target with the first-, second-, and third-order range migrations, and the complex Doppler frequency migration. In this method, after range compression, the echo signal is first transformed into the range-frequency and Doppler domain based on series reversion, and then an azimuth matched filtering procedure is implemented in the 2-D frequency domain. It can eliminate the coupling effects between range and azimuth jointly caused by the radial velocity, radial acceleration, and radial acceleration rate of a moving target. Due to the linear transform property, the proposed method can work well under low signal-to-clutter and noise ratio. In addition, the Doppler ambiguities, when target azimuth spectrum either is within a pulse repetition frequency (PRF) or spans over neighboring PRF bands, can be well solved. Both simulated and real synthetic aperture radar data processing results are provided to validate the effectiveness of the proposed algorithm.

In this paper, a new method is proposed to refocus a ground moving target in synthetic aperture radar imagery. In this method, range migration is compensated in the 2-D frequency domain, which can easily be implemented by using the complex multiplications, the fast Fourier transform (FFT), and the inverse FFT operations. Then, the received target signal in a range gate is characterized as a quadratic frequency-modulated (QFM) signal. Finally, a novel parameter estimation method, i.e., scaled generalized high-order ambiguity function (HAF), is proposed to transform the target signal into a signal on 2-D time-frequency plane and realize the 2-D coherent integration, where the peak position accurately determines the second- and third-order parameters of a QFM signal. Compared with our previously proposed generalized Hough-HAF method, the proposed method can obtain a better target focusing performance, since it can eliminate the incoherent operations in both range and azimuth directions. In addition, the proposed method is computationally efficient, since it is free of searching in the whole target focusing procedure. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

This paper proposes a new method to focus a ground moving target with complex motions and estimate its motion parameters in a synthetic aperture radar (SAR) system. In this method, the second-order Keystone transform is applied to correct the range curvature. Then, the Hough transform is applied to estimate the slope of the range walk trajectory, from which the target cross-track velocity is obtained. Finally, a generalized Hough-high-order ambiguity function (GHHAF) transform is applied to transform the target signal into a 2-D time-frequency plane and estimate its slope associated with the third-order Doppler parameter. Compared with the conventional SAR imaging methods using the second-order phase model, the proposed method can obtain better imaging quality since the third-order Doppler frequency migration is effectively eliminated. Both simulated and real data processing results are provided to validate the effectiveness of the proposed algorithm.

In synthetic aperture radar (SAR), long integration time may induce range migration and Doppler frequency migration of a received signal, which may degrade the SAR imaging performance of ground moving targets. Most of the conventional algorithms deal with the problems of range migration and Doppler frequency migration based on parameter searching. However, the exhaustive searching of target motion parameters may result in heavy computational burden. To avoid this problem, this paper proposes a new imaging method for ground moving targets without target motion parameter estimation. First, Keystone transform is applied to correct the range walk. Second, range curvature is compensated by the matched filtering function. Third, Doppler frequency migration is compensated via multiplying the data in range- and azimuth-time domains by its reversed conjugate data according to the equal interval sampling of the azimuth slow time, which avoids the searching procedure for target motion parameter estimation. Finally, the signal energy will be well accumulated in the range-Doppler domain, and thus, the moving targets can be efficiently recognized in the focused image. The major advantage of the proposed method is that it can obtain well-focused images of all targets in one processing step without target motion parameter estimation; thus, it is computationally efficient. Both simulated and real data processing results are used to validate the effectiveness of the proposed method.

In synthetic aperture radar (SAR) imaging of a ground moving target, long-time coherent integration may effectively improve the imaging quality, whereas the imaging performance may severely degrade due to the range migration and the Doppler frequency migration. In this paper, a novel motion parameter estimation method named second-order Wigner-Ville distribution (SoWVD) transform is proposed, and then, a new SAR imaging method based on the SoWVD for a ground moving target is developed. As a modified Wigner-Ville distribution method, the SoWVD method can estimate the motion parameter without the search procedure, which achieves motion parameter estimation by Fourier transform operations in the 2-D frequency plane with respect to the slow time and the delay time. In addition, it can effectively recognize the cross terms based on multiple symmetrical properties of the peaks in the 2-D frequency domain. Both simulated and real data processing results are presented to validate the proposed imaging method.