Performance of Sparse Recovery Algorithms for the Reconstruction of Radar Images From Incomplete RCS Data

Abstract

In this letter, we compare the performances of sparse recovery algorithms (SRAs) for the reconstruction of a 2-D inverse synthetic aperture radar (ISAR) image from incomplete radar-cross-section (RCS) data. The three methods considered for the SRA include the basis pursuit (BP), the BP denoising, and the orthogonal matching pursuit methods. The performances of the methods in terms of the reconstruction accuracy of the ISAR image are compared using the incomplete RCS data. In addition, traditional interpolation methods such as nearest-neighbor interpolation, linear interpolation, and spline interpolation are applied to the incomplete RCS data to reconstruct ISAR images, and their performances are compared to that of the SRAs.