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.

中文翻译：

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基于多站ISAR图像的自旋航天器动力学估计

自旋航天器的动态估计是一项挑战，在潜在空间碰撞警告等空间态势感知应用中发挥着重要作用。目前的方法基于激光和雷达传感器的遥感技术，几乎采用匹配策略来估计特定目标的动态参数和长期测量数据。这类数据驱动的方法仅仅考虑了被测特征与目标动态模式之间的内在联系，在测量收集不足的情况下很难扩展到其他航天器上。因此，本文提出了一种解释多站逆合成孔径雷达 (ISAR) 图像的新方法，用于自旋航天器的动态估计。作为雷达成像的一种独特现象，ISAR观测的成像平面不仅取决于目标与雷达相对位置的变化，而且还随着目标的自旋而变化。为了将目标动态估计与成像几何的确定解耦，采用了多站图像的角度多样性。该算法在多站观测成像投影模型下推导出了目标动态参数的显式表达式。利用混沌蚱蜢优化算法（CGOA），通过两步优化确定目标自旋运动的三个关键要素，即瞬时姿态、旋转轴和旋转速度。典型自旋航天器天宫一号（TG-I）的模拟实验，