Fast Majorize-Minimization based Super-Resolution Algorithm for Radar Forward-Looking Imaging

Abstract

Recently, super-resolution techniques have been widely used in real aperture radar superresolution imaging. In this paper, we propose a fast sparse superresolution algorithm which is based on majorize-minimization(MM) method to realize fast superresolution imaging of sparse targets in radar forward-looking area. First, we establish a model of rader forward-looking imaging and analyze the echo signal. Second, we use the majorize-minimization (MM) method to obtain the real target distribution. Due to the expensive computational cost of MM algorithm, we proposed an fast matrix inversion approach which is based on divide and conquer strategy. The superior performance of the proposed method is verified by simulations.