Focusing ISAR Images using Fast Adaptive Time-Frequency and 3D Motion Detection on Simulated and Experimental Radar Data

摘要

Optimization algorithms were developed for use with the Adaptive Joint Time-Frequency (AJFT) algorithm to reduce Inverse Synthetic Aperture Radar (ISAR) image blurring caused by higher-order target motion. A specific optimization was then applied to 3D motion detection. Evolutionary search methods based on the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm were designed to rapidly traverse the solution space in order to find the parameters that would bring the ISAR image into focus in the cross- range. 3D motion detection was achieved by using the AJTF PSO to extract the phases of 3 different point scatterers in the target data and measuring their linearity when compared to an ideal phase for the imaging interval under investigation. The algorithms were tested against both simulated and real ISAR data sets.