Self-organizing guide star selection algorithm for star trackers: Thinning method

In this study, a new self-organizing guide star selection algorithm is presented, which is a crucial part of an advanced star tracker design since the performance and reliability of star pattern recognition and attitude determination depend on the guide star selection. The problem is to minimize the total number of guide stars while ensuring at least n guide stars be measured inside a specified field of view (FOV), in every possible boresight direction. The process depends implicitly upon redundant measured stars, especially in regions of high star density. Obviously this selection process is constrained such that the stars brighter than some sensor-dictated magnitude threshold are selected for the guide star catalog. This extremely high dimensional optimization problem is impossible to solve rigorously in finite time. In this study, a heuristic approach is used to obtain the near minimum number of guide stars satisfying above constraints. The algorithm is demonstrated by generating mission catalogs for a star tracker with an 8°×8° squared FOV.