Nonparametric Bayesian 3-D ISAR Imaging of Space Debris

摘要

Space debris damage orbiting spacecraft and astronauts and ISAR imaging is an important method to recognize and classify debris. Compared with 2-D imaging, 3-D imaging is able to provide more information. However, debris with rapid spinning have great migration through range-cells, so common methods are unproductive. A novel method of ISAR 3-D imaging based on nonparametric Bayesian model is proposed aimed at debris with spinning. Firstly, a motion model and a signal model are proposed. Secondly, PSO algorithm is utilized to preprocess the data and obtain the height of target. Finally, Nonparametric Bayesian model is imposed to elaborately reconstruct the target in range and cross-range. For monostatic radar, point-target simulation data and electromagnetism data confirm that the method will obtain refined 3-D imaging results. Meanwhile, this method is capable to surmount the obstacle of Doppler aliasing and data missing caused by rapid spinning.