Moving targets always defocus and shift outside the scene in video synthetic aperture radar (video SAR) image sequences. However, the shadows of moving targets are immune to these issues and can reveal the true position of the moving targets. As such, by tracking the shadows of moving targets in the video SAR image sequence, it becomes feasible to keep track of these targets. Nevertheless, due to the small pixel size and time-varying characteristics of the target shadow, current prevailing tracking methods often prove insufficient for direct tracking of the shadow. In this paper, a shadow-assisted tracking method for moving targets based on multilevel discriminant correlation filters network (MDCFnet) is proposed. Primarily, we designed a Reverse Feature Pyramid Network (RFPN) that integrates multiple high-level features into low-level features to obtain multiple features with higher distinguishability and resolution, thereby enhancing the final tracking accuracy and precision. Furthermore, we devised Multi-level Discriminative Correlation Filters (MDCF) to perform filtering tracking under multiple feature maps. Real dataset processing results are provided to demonstrate that the proposed method outperforms other state-of-the-art methods.

Downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) cross-track dimensional imaging always suffers from incomplete observation which does not satisfy the Nyquist sampling theorem and leads to the failure of conventional 3-D frequency-domain methods. Although several sparse reconstruction-based methods have been presented to solve this problem, the basis mismatch issue in sparse reconstruction theory will degrade the image reconstruction performance. To address this issue, this article proposes a novel 3-D imaging method for DLSLA 3-D SAR, which provides another idea for 3-D imaging through sparse recovery. It utilizes recovered full-sampled data to achieve cross-track dimensional imaging instead of using the under-sampled data directly as before. The Along-track-Height plane imaging is first finished by the range-Doppler (RD) algorithm and motion error compensation. Then, an advanced nuclear norm and low-rank matrix factorization (NU-LRMF)-based matrix completion (MC) algorithm and a vector reconstruction framework are built to achieve accurate recovery of full-sampled data. Finally, the cross-track dimensional imaging is completed with recovered full-sampled data by geometric correction and beamforming. Moreover, a fast two-stage iteration strategy for NU-LRMF (TS-NU-LRMF) is also presented to accelerate convergence. The robustness and effectiveness of the proposed 3-D imaging method are verified by several numerical simulations and comparative studies based on both the complex 3-D ship model and the simulated 3-D distributed scenario.

Airborne downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) usually uses a sparse and nonuniform linear array that often does not satisfy the Nyquist sampling theorem. Therefore, the cross-track dimensional imaging will fail with the traditional 3-D frequency-domain imaging algorithms. Several grid-based sparse reconstruction (GB-SR) algorithms have been presented to solve this issue. However, they assume that the scatterers are located on the discretized grids; otherwise, the off-grid effect or basis mismatch problem will occur. To address this issue, we propose a novel hyperparameter-free gridless-based sparse reconstruction (GL-SR) algorithm (i.e., 2-D adaptive iterative reweighted atomic norm minimization algorithm called 2-D IRAN) by a combination of the optimal covariance fitting criterion and atomic norm. It is a generalized model, while the other GL-SR algorithms (e.g., GLS, RGLS, and RAM) can be interpreted as the variants of 2-D IRAN. Moreover, since the interior-point method employed in toolboxes has high computational efficiency only for the small-scale matrix optimization problem, a fast implementation of 2-D IRAN via alternating direction method of multipliers (ADMM) is presented for the large-scale matrix optimization problem. Finally, we carry out extensive numerical simulations to demonstrate the advantages and effectiveness of 2-D IRAN for DLSLA 3-D SAR imaging based on the complex 3-D ship model and 3-D distributed scenario.

High computational complexity and phase errors (PEs) are the main limitations of time-domain (TD) synthetic aperture radar (SAR) imaging algorithms. Accelerated fast backprojection (BP) (AFBP) algorithm avoids interpolation through wavenumber spectrum connection and is an efficient fast TD imaging algorithm. In order to deal with the image defocusing problem caused by PEs effectively and ensure rapid imaging, a TD autofocus method is proposed in this article, which is based on generalized sharpness metrics and the AFBP imaging model. The autofocus method is divided into two stages. First, for each subaperture (SA), the PE estimation model is established in unified polar coordinate (UPC), where the strong-scattering range-cell pixels are chosen to reduce memory burden and avoid repetitive imaging. The PE estimation is converted into a nonconvex optimization problem. Then, the genetic algorithm (GA) and the maximizing-maximum-pixel-value (MMPV) method are used to estimate the PEs. Second, SA images’ matching and constant PE’s compensation are performed to eliminate the residual PEs. The full-aperture well-focused image is obtained by the coherent accumulation of SA images. The effectiveness of the proposed method is proven by the results of simulation and real SAR data processing.

Spatial-variant phase errors (PEs) are important factors which defocus the synthetic aperture radar (SAR) image. In time-domain SAR imaging, the exact calculation of instantaneous range is carried out to realize imaging. Accurate trajectory is the key to compensate spatial-variant PEs and ensure image focus. Thus, an improved time-domain autofocus method based on three-dimensional motion errors (3-D MEs) estimation is proposed in this article. First, an improved maximizing-maximum-pixel-value method is used to estimate nonspatial-variant PEs. Meanwhile, a theoretical explanation combined with$N$-dimensional Euclidean space is described. Then, residual PEs and wrapped PEs are discussed successively. A part-overlapped partitioning scheme for sub-block images (SBIs) and a wrapped-PE model are proposed for 3-D MEs estimation. Then, the estimation problem is turned into a mixed integer programming problem, which can be solved by the combination of genetic algorithm (GA) and Tikhonov regularization. Finally, the well-focused image is obtained through updated trajectory. The effectiveness of the proposed method is proven by results of simulation and real SAR data processing.

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.