Two-dimensional random sparse sampling for high resolution SAR imaging based on compressed sensing

TL;DR: A novel SAR imaging algorithm based on compressed sensing provides the approach of receiving echo data via two-dimensional random sparse sampling with a significant reduction in the number of sampled data beyond the Nyquist theorem and with an implication in simplification of radar architecture.

Abstract: High speed analog-to-digital (A/D) sampling and a large amount of echo storage are two basic challenges of high resolution synthetic aperture radar (SAR) imaging. To address these problems, a novel SAR imaging algorithm is proposed based on compressed sensing (CS) in this paper. In particular, this new algorithm provides the approach of receiving echo data via two-dimensional (2-D) random sparse sampling with a significant reduction in the number of sampled data beyond the Nyquist theorem and with an implication in simplification of radar architecture. Then CS technique is used to reconstruct the targets in range and azimuth directions, respectively. The simulation results and error analysis show that the proposed CS-based imaging method presents many important applications and advantages which include less sampled data, lower peak side-lobe ratio (PSLR) and integrated side-lobe ratio (ISLR) and higher resolution than the traditional SAR imaging algorithm.