An Attitude Estimation Method for Space Targets Based on the Selection of Multi-View ISAR Image Sequences
Year: 2025
Type: article
Abstract: Multi-view inverse synthetic aperture radar (ISAR) image sequences provide multi-dimensional observation information about space targets, enabling precise attitude estimation that is fundamental to both non-cooperative target monitoring and critical space operations including active debris removal and space collision avoidance. However, directly utilizing all images within an ISAR sequence for attitude estimation can result in a substantial data preprocessing workload and reduced algorithm efficiency. Given the inherent overlap and redundancy in the target information provided by these ISAR images, this paper proposes a novel space target attitude estimation method based on the selection of multi-view ISAR image sequences. The proposed method begins by establishing an ISAR imaging projection model, then characterizing the target information differences through variations in imaging plane normal, and proposing an image selection method based on the uniform sampling across elevation and azimuth angles of the imaging plane normal. On this basis, the method utilizes a high-resolution network (HRNet) to extract the feature points of typical components of the space target. This method enables simultaneous feature point extraction and matching association within ISAR images. The attitude estimation problem is subsequently modeled as an unconstrained optimization problem. Finally, the particle swarm optimization (PSO) algorithm is employed to solve this optimization problem, thereby achieving accurate attitude estimation of the space target. Experimental results demonstrate that the proposed methodology effectively filters image data, significantly reducing the number of images required while maintaining high attitude estimation accuracy. The method provides a more informative sequence than conventional selection strategies, and the tailored HRNet + PSO estimator resists performance degradation in sparse-data conditions, thereby ensuring robust overall performance.
Source: Remote Sensing
DOI: 10.3390/rs17203432
Authors: Junzhi Li, Xin Ning, Dou Sun, Rongzhen Du
Institutions: Northwestern Polytechnical University, Xidian University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4415212906