Efficient ISAR Autofocus via Minimization of Tsallis Entropy

Abstract

Inverse synthetic aperture radar (ISAR) is a coherent imaging system formed by conducting signal processing on the received data consisting of radar cross section reflected from a maneuvering target. Autofocus is an essential step of ISAR imaging, whose performance has a great influence on the quality of the radar image. Minimum Shannon entropy phase adjustment (MSEPA) and minimum Renyi entropy-based algorithm (MREA) have been widely used to compensate for the phase error in ISAR autofocus. However, MSEPA and MREA have some drawbacks in terms of the noise sensitivity and computational efficiency. Tsallis entropy (nonextensive entropy) is a useful measure to describe the thermostatistical properties of physical systems. This paper concentrates on the performance of minimum Tsallis entropy phase adjustment (MTEPA) instead of the Shannon entropy. By minimizing the Tsallis entropy of an ISAR image, the MTEPA can significantly improve the computational efficiency, while retaining the image focal quality of the restored ISAR images, as compared to MSEPA and MREA. The order q of Tsallis entropy can be experimentally found by analyzing both an image quality metric and the computation time. In experimental results, the effectiveness of the MTEPA is illustrated and analyzed with simulated targets consisting of point scatterers and measured Boeing-747 data.