Joint Estimation of Satellite Attitude and Size Based on ISAR Image Interpretation and Parametric Optimization
Jiadong Wang, Yachao Li, Lan Du, Ming Song, Mengdao Xing
Abstract
This article presents a novel approach for the joint estimation of satellite attitude and size based on inverse synthetic aperture radar (ISAR) image interpretation and parametric optimization. The satellite’s solar panel, which is segmented from the ISAR image by employing pix2pix generative adversarial network (Pix2pixGAN), is chosen for investigation in this article due to its unique rectangular structure. We innovatively use the principal component analysis (PCA) to explore the satellite solar panel’s structural features in an ISAR imagery. The projection matrix is then established to link the extracted features and the satellite’s absolute attitude and size. Parametric optimization is established based on the relationship between the extracted features and the satellite’s absolute attitude and size. A Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based fast iterative search algorithm is employed to search the satellite’s absolute attitude and size simultaneously through an iterative approach. The simulation data are generated from actual satellite orbital parameters and computer-aided-design (CAD) models of the Aqua satellite in the experiments. Simulation experiments verify the effectiveness of the proposed method.