A Rotational Motion Parameter Estimation Method for Slow-Spinning Space Targets Utilizing Similarity Analysis of Sequential ISAR Images

Uncontrolled satellites and other space targets usually exhibit slow-spinning motion. Accurately estimating their rotational motion parameters (RMP) is of great significance for uncontrolled state analysis. To address this challenge, a method is proposed herein to estimate the RMP of slow-spinning space targets, based on similarity analysis of sequential inverse synthetic aperture radar (ISAR) images. Firstly, an ISAR imaging model for slow-spinning space targets is constructed. It is then derived that two adjacent ISAR images exhibit an approximate planar rotational geometric relationship, and the planar rotation matrix in this relationship is directly associated with the RMP. Then, RMP, namely the rotation axis and angular velocity, are taken as optimization variables, while the similarity between successive ISAR images serves as the objective function. In this manner, the estimation of RMP is constructed as an unconstrained optimization problem. Ultimately, the particle swarm optimization (PSO) algorithm is utilized to estimate the RMP. Without depending on any prior knowledge about the target's structure, the proposed method can realize RMP estimation. Experimental results obtained from electromagnetic simulation data demonstrate its effectiveness and robustness.