Robustness and Efficiency Improvements for Star Tracker Attitude Estimation

In this paper, a star tracker attitude estimation procedure with increased robustness and efficiency, using the AIM (Attitude Estimation using optimal Image Matching) algorithm, is presented and validated. The unique approach of the AIM algorithm allows us to introduce a reliable quality check which can be efficiently calculated. Unlike existing validation methods, this quality check not only detects that some of the data is unreliable, it also determines which star measurements are unreliable. These unreliable measurements can be removed from the data set and a new attitude quaternion can be calculated without having to repeat the entire AIM algorithm. This greatly improves the robustness of the attitude estimation, while limiting the computational expense. Furthermore, the structure of AIM allows us to reuse previously calculated data when the change in attitude between subsequent measurements is small. This way, the efficiency of the entire attitude estimation cycle can be increased significantly. These enhancements are validated with simulated star tracker data, which show that for pointing maneuvers, the computational cost can be reduced by more than 40% compared to the state-of-the-art procedure. The results show that the improvements significantly improve the robustness and lower the computational cost of the star tracker attitude estimation. As a consequence, the overall performance of the attitude determination and control system greatly increases. The increased efficiency of the attitude estimation could also allow the use of star trackers in smaller satellite