Dynamic Estimation of Spin Spacecraft Based on Multiple-Station ISAR Images

Abstract

Dynamic estimation of spin spacecraft is a challenge and plays a significant role in space situation awareness applications like potential space collision warning. Based on remote sensing technologies of laser and radar sensors, current methods almost adopt a match strategy to estimate the dynamic parameters of a particular target with the long-term measurement collection. These kinds of data-driven methods merely consider the inherent connection between the measured characters and target dynamic patterns, and can hardly be expanded to other spacecraft when the measurement collection is insufficient. Therefore, this article presents a novel approach to interpreting multiple-station inverse synthetic aperture radar (ISAR) images for the dynamic estimation of spin spacecraft. As a unique phenomenon of radar imaging, the imaging plane of ISAR observation not only depends on the change of the relative position between the target and radar, but also changes with the spin of the target. In order to decouple the target dynamic estimation from the determination of the imaging geometry, the angular diversity of multiple-station images is employed. The proposed algorithm deduces an explicit expression of target dynamic parameters under the imaging projection model of the multiple-station observation. By utilizing the chaotic grasshopper optimization algorithm (CGOA), it determines three crucial elements of the target spin motion with a two-step optimization, including instantaneous attitude, rotation shaft and rotation speed. Simulation experiments of a typical spin spacecraft, Tiangong-I (TG-I), illustrate the feasibility of the proposed method under different motion patterns.