Inverse Synthetic Aperture Radar Imaging Based on the Non-Convex Regularization Model

Abstract

Compressed Sensing (CS) has been shown to be an effective technique for improving the resolution of inverse synthetic aperture radar (ISAR) imaging and reducing the hardware requirements of radar systems. In this paper, our focus is on the l_p 0 p 1 model, which is a well-known non-convex and non-Lipschitz regularization model in the field of compressed sensing. In this study, we propose a novel algorithm, namely the Accelerated Iterative Support Shrinking with Full Linearization (AISSFL) algorithm, which aims to solve the l_p regularization model for ISAR imaging. The AISSFL algorithm draws inspiration from the Majorization-Minimization (MM) iteration algorithm and integrates the principles of support shrinkage and Nestrove's acceleration technique. The algorithm employed in this study demonstrates simplicity and efficiency. Numerical experiments demonstrate that AISSFL performs well in the field of ISAR imaging.