Sparse Inverse Synthetic Aperture Radar Imaging Using Structured Low-Rank Method
Gang Xu, Bangjie Zhang, Jianlai Chen, Fan Wu, Jialian Sheng, Wei Hong
Abstract
There has been an increasing interest in addressing the issue of high-resolution inverse synthetic aperture radar (ISAR) imaging from sparse sampling data. Traditional compressed sensing (CS) and matrix completion (MC) methods are based on sparse and low-rank constraints, respectively, which do not make full use of the structure of ISAR data. In this article, a sparse ISAR imaging algorithm using a structured low-rank approach is proposed for enhanced imaging performance. Based on the observation that the structured Hankel matrix has better low-rank property, the proposed algorithm can outperform the group of conventional MC methods in terms of accuracy to data quality and quantity. Rather than using the traditional singular value decomposition (SVD) solution of nuclear norm minimization, the proposed algorithm restates the nuclear norm via an equivalent reformulation that the structured Hankel matrix can be decomposed into two disjointed parts to avoid the dimensional expansion of the Hankel matrix. Meanwhile, the alternative direction method of multipliers (ADMMs) is applied to effectively reduce the computational complexity. Finally, the effectiveness of the proposed algorithm is further validated using the experiments on simulated and measured data.