Fast Entropy Minimization Based Autofocusing Technique for ISAR Imaging

摘要

Autofocusing technology is an essential step of Inverse Synthetic Aperture Radar (ISAR) imaging, whose performance has great influence on the quality of the radar image. As far as the existed autofocusing methods are concerned, the methods based on minimum entropy criterion are robust and have been widely applied in both Synthetic Aperture Radar (SAR) and ISAR imaging. However, the Minimum Entropy based Autofocusing (MEA) methods usually suffer from heavy computation burden because of the complex formula of image entropy and the optimal search of the phase error. In this paper, a novel fast MEA method based on the Newton method is proposed. Moreover, the Simplified Newton (SN) method and the Modified Newton (MN) method are also introduced to the implement of MEA, and provide high computation efficiency. Mathematical analysis and experimental results based on real measured data of two airplanes have validated the high computation efficiency of the proposed methods. Compared to the widely applied MEA method for ISAR imaging, the proposed methods can improve the computation efficiency by 10 to 20 times.