Compressive sensing SAR range compression with chirp scaling principle

Abstract

Compressive sensing (CS) techniques offer a framework for the detection and allocation of sparse signal with a reduced number of measurements. This paper proposes a novel SAR range compression, namely compressive sensing with chirp scaling (CS-CS), achieving the same range resolution as conventional SAR approach, while using fewer range samplings. In order to realize accurate range cell migration correction (RCMC), chirp scaling principle is used to construct reference matrix for compressive sensing recovery. Additionally, error diagrams are designed for measurement of the performance of CS-CS, and some experiments of using real data are performed to deal with the errors caused by three conditions: SNR, sparsity and sampling.