Signal Processing Issues in ISAR Imaging

In the previous chapters, we have dealt with the geometrical aspects of inverse synthetic aperture radar (ISAR) imaging, which have led to a theoretical approach of the problem of forming electromagnetic (EM) images of noncooperative targets using high-resolution radars. Nevertheless, real-world data are corrupted by noise, and clutter and targets usually undergo complex motion, which cannot easily be modeled or predicted. Moreover, other effects such as limited resolution or high sidelobe levels (SLLs) may strongly reduce the effectiveness of ISAR imaging in classification and recognition. In this chapter, we will introduce such problems and provide both classic and recent solutions to them.