Data-Driven Motion Compensation Techniques for Noncooperative ISAR Imaging

We consider the data-driven motion compensation problem in inverse synthetic aperture radar (ISAR) imaging. We present optimization-based ISAR techniques and propose improvements to the range alignment, time-window selection, autofocus, time–frequency-based image reconstruction, and cross-range scaling procedures. In experiments, the improvements reduced the computational burden and increased the image contrast by 50% at best and 28% on average in several test cases, including changing translational and rotational motion.