Application of compressed sensing in sparse aperture imaging of radar

Abstract

A new optimal reconstruction method based on compressed sensing (CS) for sparse synthetic aperture radar (SAR)/ inverse SAR (ISAR), which can be used in widely sparse aperture, is proposed in this letter. Unlike other parametric estimation method as all-pole algorithm, CS can obtain near-optimal estimation and global-minimal error of gapped signal representation with structured dictionaries and random projections. To resolve the issue of minimization of non-zeros elements, traditional approaches such as orthogonal matching pursuit (OMP) and basis pursuit (BP) may be used. This non-adaptive means performs better than FFT imaging, especially in azimuth focusing of SAR/ISAR. The results with simulation data and real sparse SAR/ISAR data validate the feasibility and superiority of the approach.