A star extraction method of dynamic star image for low signal-to-noise ratio based on joint probability estimation of optical flow-trajectory

Abstract

The star sensor is an attitude-sensitive device for spaceflight. It is a critical component in the autonomous attitude determination of aerospace vehicles. Compared to other attitude sensors, the star sensor offers higher attitude accuracy, low power consumption, small volume, and strong autonomy. It plays an important role in high-precision remote sensing, astronomical navigation, and other fields. Star extraction is an essential part of the star sensor in the process of working. Its accuracy and the number of extracted stars affect the performance of the star sensor. This paper proposes a method of star extraction based on the combination of the Improved Optical Flow Method (IOFM) and Dynamic Filtering (DF) named IOFM-DF. Based on the optical flow method, the motion characteristics of stars in the time and space domains are considered. Due to the difference between the star and noise in the motion trajectory, dynamic filtering is used to reduce the influence of noise from the star image on the extraction effect of the star. Considering the statistical properties of the motion trajectories of multiple stars, the cosine distance of the motion track between the extracted point and the star is calculated to predict the probability that the extracted point belongs to the star. IOFM-DF can extract and track stars in the star image for a low signal-to-noise ratio. Experimental results show that IOFM-DF increases the number of star extractions by at least 30% compared to traditional methods. This research is important to improve the accuracy and performance of star sensors.