A Hierarchical Motion Representation for Motion Estimation of Instability Space Targets in Linear Imaging System

The linear laser imaging radar is an ideal tool to precisely measure long-range and large-scale space targets due to its high imaging resolution and wide field of view. However, challenges arise due to inherent two-scale motion difference of intraframe and interframe within linear imaging systems, especially under the noncooperative conditions. To address this hurdle, we present a hierarchical motion representation framework for estimating the motion of instability space targets. First, we devise a progressive motion estimation mechanism rooted in a transmission model (TM) tailored to the interframe differences among consecutive point cloud sequences captured at regular time intervals through the utilization of matrix representations. This mechanism breaks down the estimation of compound motion—incorporating spin and precession into two distinct rotational motion estimations, significantly mitigating the impact of intraframe motion difference. Then, an iterative convergence strategy is introduced to counteract the impact of intraframe motion difference and subtle shape variations among point cloud sequences by leveraging physical motion parameters. Experimental results have illustrated that our approach consistently has attained a higher level of estimation accuracy, with errors less than the order of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-{2}}$ </tex-math></inline-formula> using 13 consecutive frames in the majority of cases.