An information divergence measure for ISAR image registration
Entropy-based divergence measures have shown promising results in many areas of engineering and image processing. A generalized information-theoretic measure called Jensen-Renyi divergence is proposed. Some properties such as convexity and its upper bound are derived. Using the Jensen-Renyi divergence, we propose a new approach to the problem of ISAR (inverse synthetic aperture radar) image registration. The goal is to estimate the target motion during the imaging time. Our approach applies Jensen-Renyi divergence to measure the statistical dependence between consecutive ISAR image frames, which would be maximal if the images are geometrically aligned. Simulation results demonstrate a much improved performance of the proposed method in image registration.