ABSTRACT
This chapter provides a common framework for the application of compressive sensing (CS) to inverse synthetic aperture radar (ISAR) imaging. Specifically, it provides an ISAR signal model in the framework of CS-based reconstruction. It is worth pointing out that the bistatic geometry can be used to model the received signal in either the active or the passive radar system, and for both monostatic and bistatic configurations. An ISAR image can be obtained by means of the conventional range–Doppler (RD) algorithm applied to the reconstructed data and can be fairly compared with the image obtained via RD algorithm applied to the original data. The chapter reviews the application in which CS-based reconstruction allows an enhancement of performance with some results. It demonstrates the effectiveness of CS-based method and its performances against different SNR values by using the image contrast as image quality criteria on real data. The chapter outlines a comparison between CS and other super-resolution (SR) techniques.
