Three-dimensional (3-D) synthetic aperture radar (SAR) near-field imaging provides a novel radar cross section (RCS) measurement method, which has been widely concerned in the microwave field. However, the accuracy of RCS measurement based on image is easily affected by sidelobes of matching filtering (MF) imaging and background clutter. Although the existing observation-matrix-based sparse SAR imaging algorithm can suppress sidelobes and clutter, it requires huge computational cost. To solve the above problems, an RCS measurement method using sparse imaging based 3-D SAR complex image is proposed. First, 3-D sparse imaging based complex image is used to suppress sidelobe and clutter of MF image. After that, the far-field RCS of the target is obtained by the compensation factor. Compared with the observation-matrix-based sparse SAR imaging algorithm, the proposed algorithm can reduce the computation time while retain the phase information of scene distribution. Compared with the RCS measurement based on MF image, the RCS measurement performance of the proposed method is improved. The simulation and real data of the system results validate the proposed method.

In recent years, three-dimensional (3D) synthetic aperture radar (SAR) imaging has proved its great potential in monitoring, security inspection and radar cross-section (RCS) measurement. However, 3D SAR images based on matched filter (MF) methods have high sidelobes and are susceptible to background noise. Therefore, in this article, we propose a novel 3D sparse SAR imaging method to improve image quality, which combines the Plug-and-Play framework and the improved alternating direction method of multiplier (ADMM). First of all, Plug-and-Play framework allows one to use state-of-the-art denoisers instead of proximal operators to improve image quality. Secondly, we linearize the subproblem of ADMM involving forward imaging model. Compared with the traditional ADMM method, the improved ADMM, namely linear ADMM (LADMM), avoids the inversion of high-dimensional matrix and is more suitable for solving high-dimensional imaging problems. Simulation and real data experiments show that the proposed method can effectively improve the image quality. Numerical analysis and 3D visualization results are presented, which prove the impressive performance of Plug-and-Play LADMM.

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized $L_q$-regularization are proposed. First, we combine majorization–minimization (MM) and $L_1$ regularization (MM-$L_1$) to improve SAR image quality. Next, we combine MM and $L_{1/2}$ regularization (MM-$L_{1/2}$) to achieve high-quality 3D images. Then, we present the algorithm which combines MM and $L_0$ regularization (MM-$L_0$) to obtain 3D images. Finally, we present a generalized MM-$L_q$ algorithm (GMM-$L_q$) for sparse SAR imaging problems with arbitrary $q\left(0\le q\le 1\right)$ values. The proposed algorithm can improve the performance of 3D SAR images, compared with existing regularization techniques, and effectively reduce the amount of calculation needed. Additionally, the reconstructed complex image retains the phase information, which makes the reconstructed SAR image still suitable for interferometry applications. Simulation and experimental results verify the effectiveness of the algorithms.

Array synthetic aperture radar (SAR) three-dimensional (3D) imaging can obtain 3D information of the target region, which is widely used in environmental monitoring and scattering information measurement. In recent years, with the development of compressed sensing (CS) theory, sparse signal processing is used in array SAR 3D imaging. Compared with matched filter (MF), sparse SAR imaging can effectively improve image quality. However, sparse imaging based on handcrafted regularization functions suffers from target information loss in few observed SAR data. Therefore, in this article, a general 3D sparse imaging framework based on Regulation by Denoising (RED) and proximal gradient descent type method for array SAR is presented. Firstly, we construct explicit prior terms via state-of-the-art denoising operators instead of regularization functions, which can improve the accuracy of sparse reconstruction and preserve the structure information of the target. Then, different proximal gradient descent type methods are presented, including a generalized alternating projection (GAP) and an alternating direction method of multiplier (ADMM), which is suitable for high-dimensional data processing. Additionally, the proposed method has robust convergence, which can achieve sparse reconstruction of 3D SAR in few observed SAR data. Extensive simulations and real data experiments are conducted to analyze the performance of the proposed method. The experimental results show that the proposed method has superior sparse reconstruction performance.

Three-dimensional (3D) synthetic aperture radar (SAR) images can provide comprehensive 3D spatial information for environmental monitoring, high dimensional mapping and radar cross sectional (RCS) measurement. However, the SAR image obtained by the traditional matched filtering (MF) method has a high sidelobe and is easily disturbed by noise. In order to obtain high-quality 3D SAR images, sparse signal processing has been used in SAR imaging in recent years. However, the typical $L_1$ regularization model is a biased estimation, which tends to underestimate the target intensity. Therefore, in this article, we present a 3D sparse SAR image reconstruction method combining the Cauchy penalty and improved alternating direction method of multipliers (ADMM). The Cauchy penalty is a non-convex penalty function, which can estimate the target intensity more accurately than $L_1$. At the same time, the objective function maintains convexity via the convex non-convex (CNC) strategy. Compared with $L_1$ regularization, the proposed method can reconstruct the image more accurately and improve the image quality. Finally, three indexes suitable for SAR images are used to evaluate the performance of the method under different conditions. Simulation and experimental results verify the effectiveness of the proposed method.

Linear array synthetic aperture radar (SAR) can achieve three-dimensional (3D) high-resolution imaging, which provides a novel scattering diagnosis technique. However, high sidelobes and background noise are the main challenges of scattering diagnosis based on SAR image. Sparse imaging can improve image quality, such as suppressing sidelobes and noise. $L_{1}$ regularization is an efficient and typical model for sparse imaging. However, the $L_{1}$ regularization, as a convex optimization method, often introduces bias in amplitude estimation, which has a negative impact on scattering diagnosis. Therefore, in this paper, 3D imaging method based on complex-valued minimax concave penalty (CMCP) and improved alternating direction method of multipliers (IADMM) is presented to obtain high-quality and high-accuracy 3D SAR images for scattering diagnosis. Compared with the existing sparse imaging method based on $L_{1}$ regularization, the proposed method not only improves the image quality, but also reduces the bias effect, which can be applied for scattering imaging. Additionally, IADMM significantly reduces computational complexity. The experimental results indicate that the proposed method has compelling reconstruction accuracy of SAR scattering image.