A 3-D Sparse SAR Imaging Method Based on Plug-and-Play
Yangyang Wang, Zhiming He, Xu Zhan, Qiangqiang Zeng, Yunqiao Hu
Abstract
In recent years, 3-D synthetic aperture radar (SAR) imaging has proved its great potential in monitoring, security inspection, and radar cross section (RCS) measurement. However, 3-D 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 3-D sparse SAR imaging method to improve the image quality, which combines the plug-and-play framework and the improved alternating direction method of multiplier (ADMM). First, the plug-and-play framework allows one to use state-of-the-art denoisers instead of proximal operators to improve the image quality. Second, 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 3-D visualization results are presented, which prove the impressive performance of plug-and-play LADMM.