Motion compensation for Compressive Sensing SAR imaging with autofocus

Abstract

The high resolution required by various modes of Synthetic Aperture Radar (SAR) results in a huge amount of sampling data, which brings a demand for bigger storage. Recently, a novel SAR concept based on Compressive Sensing (CS) theory asserts that an unknown sparse signal can be recovered exactly with an overwhelming probability even from what appear to be highly sub-Nyquist-rate samples. In this paper, the phase errors caused by radar platform motion are discussed for CS based SAR imaging, and autofocus processing is employed to implement motion compensation. The experiment results show that unfocused SAR images through CS recovery are different from conventional unfocused SAR images for the same motion caused phase error, and usually used Phase Gradient Autofocus (PGA) algorithm is still effective to CS based SAR motion compensation.