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

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

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

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

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

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

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

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