Sparsity-Driven ISAR Imaging Based on Two-Dimensional ADMM
Hamid Reza Hashempour
Abstract
Compressed sensing (CS) can achieve high resolution inverse synthetic aperture radar (ISAR) imaging of moving targets with limited measurements. Recently, alternating direction method of multipliers (ADMM) has been introduced to solve the optimization problem for one dimensional (1D) sparse signal recovery. The main drawback of 1D sparsity-driven algorithms are the high memory usage and the computational complexity. Thus, in this paper a novel two dimensional (2D) ADMM approach is presented which can be directly applied to the ISAR model in matrix form, and needs lower memory and computations compared to the 1D algorithm. Moreover, the performance of the 2D-ADMM method is better than the 2D smoothed L0 (2D-SL0) and 2D gradient projection sequential order one negative exponential (2D-GP-SOONE) algorithms in different signal-to-noise ratio (SNR) conditions and sampling rates. Joint simulations and measured data results based on real data of Yak-42 airplane, validate the superiority of the proposed approach.