An Augmented $H_\infty$ Filter for Satellite Jitter Estimation Based on ASTER/SWIR and Blurred Star Images

Abstract

Remote sensing images obtained from onboard linear array cameras suffer from geometric disturbances in the presence of attitude jitter from a satellite platform. Thus, platform jitter estimation is essential for improving satellite stability and enhancing remote sensing image quality. In this article, a novel integration framework is designed to estimate attitude fluctuation, which combines multispectral advanced spaceborne thermal emission and reflection/short-wave-infrared (ASTER/SWIR) data and blurred star images from onboard optical sensors. First, a multilevel XGBoost algorithm is applied to learn the nonlinear relationship between the star blurring and instantaneous jitter displacement. An image registration method and a deconvolutional process are then introduced to remove the satellite jitter from ASTER/SWIR data. Next, an augmented $H$$_\infty$ filter is proposed to fuse and estimate the satellite jitter from two algorithms. Simulating experiment results indicate that the jitter estimation error of the proposed integration framework is reduced by 40%. Compared with the existing jitter estimation methods only based on remote sensing image processing, our strategy has better robustness and accuracy, especially on extreme ground scenes.