Due to its rapid data acquisition capabilities, the Along-Track Swarm SAR (ATS-SAR) system offers a distinct advantage in simplifying the motion states of moving target. This paper addresses challenges encountered in ATS-SAR moving target imaging, specifically focusing on issues related to partial aperture data loss and the spatiotemporal non-equivalence of the ATS-SAR moving target echo. A novel ATS-SAR imaging method for moving target is introduced. Initially, the ATS-SAR moving target echo model is established. Subsequently, the proposed algorithm designs a phase compensation function that contributes to the accurate reconstruction of the complete echo for ATS-SAR moving target, resulting in outstanding imaging performance.

Circular synthetic aperture radar (CSAR) has garnered a lot of attention because of its exceptional capabilities. However, CSAR imaging is sensitive to terrain undulation errors due to the curved trajectory. The projection of an object whose height deviates from a reference height (RH) forms a circular ring in the full-aperture image. Presently, error compensation using a digital elevation model (DEM) is a prevalent solution to this issue. Although DEM can be inverted from echo data, the accuracy is unsatisfactory. Inspired by optical multi-focus image fusion methods, a CSAR multi-layer focusing imaging method is proposed in this letter. Firstly, CSAR sub-aperture images are used to construct multi-layer focusing images by displacement compensation, which is more efficient than applying an imaging algorithm directly. Secondly, the multi-focus image fusion method is applied to obtain a fully focusing image. Finally, the decision maps are used to invert the target region’s DEM. The multi-layer focusing imaging method can make objects of different heights focused. The fused result is fully focused and has a resolution of 1 m, depending on the CSAR system. The inverted DEM can visually describe the objects’ contours. Experimental results demonstrate the effectiveness and practicability of the proposed method.

A unique mode known as wide-angle staring synthetic aperture radar ground moving target indication (WasSAR-GMTI) allows for the dynamic surveillance of moving targets over a wide range of azimuth angles. Despite WasSAR-GMTI develops rapidly, the parameter estimation and trajectory reconstruction of moving target in a three-Dimensional (3-D) field have not been solved well in WasSAR. In order to address this problem, a framework based on 3-D velocities’ and 3-D positions’ estimation is proposed in this article. On account of the derived equivalence, it is possible to characterize moving targets in WasSAR-GMTI and achieve the decoupling of velocity and position. First, for the multidimensional velocities’ estimation, the joint interferometric phases of several subapertures are utilized. Then, to estimate the multidimensional positions, the signal of moving target in WasSAR is modeled as a polynomial-phase signal (PPS). We tackle the issue of 3-D positions’ estimation through the coefficients of PPS estimated by cubic phase function (CPF) method. Finally, 3-D trajectory reconstruction of moving target is accomplished by combining the results of multiaperture estimation. The proposed method extends the parameter estimation to the 3-D case, and the applications of WasSAR-GMTI are spread. Moreover, the parameter estimation of moving target is addressed independently, namely without auxiliary information of priori road detail. The estimation accuracy of the proposed method is evaluated by the simulated data, and the validity and feasibility of the proposed method are demonstrated through the results of real data.

The low-band synthetic aperture radar (SAR) is a system that can present foliage-penetrating (FP) targets and exposed strong-scattering targets while the high-band SAR system can present exposed targets and texture information except for FP targets. Therefore, the different features of co-registered low-band and high-band SAR images can theoretically reveal FP targets with some disturbances. However, the co-registered dual-band SAR images cannot do the difference operator directly for the distinct statistical property of the SAR image pair. The traditional method is to transfer the background texture from the high-band SAR image to the low-band SAR image by the cycle-consistent generative adversarial network (CGAN) method. However, CGAN would generate discontinuity and mistakes in transferring the large-scale texture information, and the complex network would heavily increase the time complexity of the application. To overcome this issue, we first innovatively introduce a fast and accurate texture-transferring method based on the dual-band SAR image fusion (DBIF), then we further combine the DBIF-based texture-transferring method with the difference operator and the threshold segmentation, and finally we get a novel DBIF-based FP target detection (DBIFFPTD) method. To verify the feasibility of the DBIF-based texture transfer method, we discuss the reflection form of different landscape objects at dual-band waves and estimate the theoretical texture transferring the image. Experiments on the open dual-band AIR-MD-SAR dataset and the independently measured dual-band SAR dataset show that the novel DBIF performs better than CGAN in transferring texture information of dual-band SAR images. Besides, dual-band linear SAR (LSAR) and circular SAR (CSAR) FP experiments are both conducted to show that the proposed DBIFFPTD has wide applicability in the flight track and is superior to the traditional low-band double-parameter constant false alarm rate (DCFAR) detector-based FPTD (DCFARFPTD) method and the dual-band CGAN-based FPTD (CGANFPTD) method.

The performance of multi-channel synthetic aperture radar (SAR)-based ground moving target indication (GMTI) is inevitably restricted by channel imperfections and imbalances, and channel calibration procedure is incorporated into the scheme accordingly. The adaptive two-dimensional calibration (A2DC) algorithm is sensitive to outliers and suffers from correction errors. To solve the problem, a modified A2DC (MA2DC) algorithm is proposed in this letter. By improving Doppler calibration model, the proposed MA2DC is more robust than the A2DC algorithm. It can effectively suppress clutter while maintaining detailed features of moving targets, after which the detection capability of moving targets in SAR-GMTI is improved. Its performance is demonstrated by the Gotcha data, which is publicly released by Air Force Research Laboratory.

One-bit synthetic aperture radar (SAR) imaging has garnered significant interest due to its ability to lower the cost of storing enormous amounts of data during sampling and transmission, as well as the expense of analog-to-digital converters (ADCs). However, existing one-bit SAR imaging methods suffer from high computational complexity and artifacts in the resulting images. To address these problems, the sparse logic regression model (SLR) solved by iterative hard threshold (IHT) is applied to one-bit SAR imaging, and a new SLR-IHT imaging method is proposed. The SLR-IHT method models the one-bit SAR imaging problem as an SLR task and optimizes the solution using the IHT framework. By leveraging the joint sparsity of the real and imaginary components, the proposed method enhances imaging quality while effectively suppressing artifacts. To accelerate computation, the Armijo step size criterion is employed to adjust the step size and support set during the iterative procedure. Moreover, a theoretical investigation into the convergence properties of the proposed method was conducted. Extensive simulations and real data experiments are conducted to evaluate the performance of the SLR-IHT method. The results demonstrate its superiority over existing one-bit SAR imaging techniques in terms of imaging quality and computational efficiency.

In order to solve the problem of SAR imaging with azimuth missing data, a novel missing data SAR imaging algorithm is proposed in this paper. In the algorithm, the complete echo can be recovered at the two-dimensional frequency-domain by using the generalized orthogonal matching pursuit (GOMP) algorithm. The simulation result verifies the effectiveness of the proposed algorithm. Since the proposed algorithm only needs to recover the echo corresponding to sparse target-located range gates, compared with the state-of-the-art SAR imaging algorithm with azimuth missing data, it shows the advantage in the computational complexity when the targets are sparse enough in the range direction and it has a better imaging performance than the state-of-the-art azimuth missing data algorithm in noiseless and noisy settings.

Synthetic aperture radar (SAR) raw data missing occurs when the radar is interrupted for various reasons during the work. Different solutions have been proposed to this problem. In recent years, with the continuous deepening of the research on compressed sensing (CS), it has also been fully utilized in solving the problem of missing data. When using traditional greedy algorithms to recover missing data, we need to know the sparsity, but it is often unknowable in practice. This letter proposes to apply the sparsity adaptive segmented orthogonal matching pursuit (SAStOMP) algorithm to the recovery of SAR missing data. Simulation results show that the proposed method can recover missing SAR data under the condition of unknown sparsity and can adapt to a wider range of threshold parameters. It has good recovery performance for periodic and nonperiodic missing SAR raw data, thus improving SAR imaging results.

Recently, we have proposed the coprime tomographic synthetic aperture radar (TomoSAR) technique, whose baseline configuration conforms to the coprime array geometry. This technique is devoted to reducing the required number of acquisitions in the practical application where the number of flight passes is usually restricted due to cost consideration and temporal decoherence. This letter extends the tomographic reconstruction of the coprime TomoSAR to the atomic norm minimization (ANM) framework to pursue super-resolution. A compact ANM approach is proposed in this letter for the tomographic reconstruction of coprime TomoSAR in the presence of multiple looks data. Compared with the conventional ANM approach, the proposed approach compresses the dimension of the ANM model to a smaller size by two operations. One operation is that the equivalent covariance matrix is constructed to be conformed with the covariance matrix of the real acquisition data. The other operation is adopting the singular value decomposition (SVD) technique to reduce the look dimension of acquisition data. As a result, the compact approach reduces the computation complexity without performance loss. It is confirmed by simulation experiments.

For synthetic aperture radar (SAR) images, there are many defocused moving targets with azimuthal deviation. Ground moving target indication (GMTI) is developing rapidly to detect moving target for traffic surveillance and military vehicle tracking. In this paper, multi-channel SAR-GMTI technology is studied and a moving target detection method based on clutter suppression interference (CSI) and relaxation-based cyclic (RELAX) is proposed to detect moving target efficiently and accurately, the time of which is only 0.22% of the time by global RELAX. Combined with multi-channel signals, CSI is performed to detect targets of interest. Then, RELAX is applied to further reduce false alarm and estimate radial velocity for geolocation. Experimental SAR data processing results show that the method is effective for moving target detection and parameter estimation.

Holographic synthetic aperture radar (HoloSAR) tomography is an attractive imaging mode that can retrieve the 3-D scattering information of the observed scene over 360° azimuth angle variation. To improve the resolution and reduce the sidelobes in elevation, the HoloSAR imaging mode requires many passes in elevation, thus decreasing its feasibility. In this article, an imaging method based on iterative adaptive approach (IAA) and generalized likelihood ratio test (GLRT) is proposed for the HoloSAR with limited elevation passes to achieve super-resolution reconstruction in elevation. For the elevation reconstruction in each range-azimuth cell, the proposed method first adopts the nonparametric IAA to retrieve the elevation profile with improved resolution and suppressed sidelobes. Then, to obtain sparse elevation estimates, the GLRT is used as a model order selection tool to automatically recognize the most likely number of scatterers and obtain the reflectivities of the detected scatterers inside one range-azimuth cell. The proposed method is a super-resolving method. It does not require averaging in range and azimuth, thus it can maintain the range-azimuth resolution. In addition, the proposed method is a user parameter-free method, so it does not need the fine-tuning of any hyperparameters. The super-resolution power and the estimation accuracy of the proposed method are evaluated using the simulated data, and the validity and feasibility of the proposed method are verified by the HoloSAR real data processing results.

Holographic synthetic aperture radar tomography can realize full 3-D reconstructions of objects over 360° with very high resolution, and thus becomes an interesting 3-D imaging technique. However, due to the uncompensated platform motion errors, phase errors among different tracks usually exist in this imaging mode, which will hinder the 3-D image focusing. In this letter, a phase calibration method based on phase gradient autofocus is proposed to mitigate the influence of these phase errors. It exploits the space-invariant characteristic of the phase errors in a limited scene and uses an efficient and robust multibaseline autofocus to calibrate the phase errors among the multiple circular tracks. Experimental results with the GOTCHA real data are carried out to demonstrate the validity and feasibility of the proposed method.

The ultrawideband (UWB) bistatic forward-looking synthetic aperture radar (BFSAR) can realize the high-resolution imaging in the forward direction of the transmitter/receiver, so it has significant applications in both civil and military fields. However, compared to the general narrowband side-looking bistatic synthetic aperture radar systems, the UWB BFSAR systems have larger fractional signal bandwidth and more special acquisition geometry, which will affect the behavior of spatial resolutions. Current methods for spatial resolution analysis of the UWB BFSAR are unsuitable because they do not consider the effects of these two special issues. This letter analyzes the special behavior of spatial resolutions in the UWB BFSAR, and the straightforward gradient method is utilized and extended based on the analysis to calculate the spatial resolutions. The theoretical analysis shows that the range resolution can be calculated by the traditional gradient method directly but the Doppler resolution should be calculated by the extended method. Simulated results verify the correctness of the theoretical analysis and the validity of the proposed method.