ISAR Imaging by Two-Dimensional Convex Optimization-Based Compressive Sensing

Abstract

A novel 2D convex optimization-based compressive sensing (CS) method is presented for inverse synthetic aperture radar (ISAR) imaging. The method deals directly with the 2D signal model for the image reconstruction based on solving a convex optimization problem. The superiority of this method is that the memory usage and the computational complexity are much lower than those of the 1D CS-based ISAR imaging method. Especially, the method belongs to a convex optimization problem. Convex functions do not suffer from local minima assuring that the achieved solution always happens to be the optimal. Simulation and experimental results are provided to demonstrate the performance of the proposed method with comparisons to the traditional Fourier-based method and to the smoothed L0-norm-based method, which proves that the proposed method is an effective way to solve the ISAR imaging problem.