Motion modeling and blurred image simulation of the star tracker used for deep-space missions

The star tracker is the most accurate attitude measurement device among different types of attitude measurement devices. It is based on captured star images to extract star centroids and then determine attitude. However, images taken by the star tracker under dynamic conditions are often blurred, which limits its dynamic performance. In this paper, we first classify and model motions of the star tracker under dynamic conditions, including rotations and angular vibrations. Then, a motion kernel consisting of three individual descriptors (trajectory, intensity, and point spread function) is proposed to model the motion blurring process and simulate blurred images. Experimental results show that our approach can model a wide variety of blurs including uniform and non-uniform blurs, and it can provide extremely realistic blurred images. These findings are crucial to the subsequent centroid extraction of stars and the dynamic performance of the star tracker. (c) 2022 Optica Publishing Group

Automated summary

This study investigates the issue of image blurring in star tracker under dynamic conditions and proposes a model-based approach to simulate and generate realistic blurred images. Experimental results demonstrate that the proposed method can effectively simulate various types of image blurs and plays a crucial role in the centroid extraction of stars and the dynamic performance of the star tracker.