Neural-Network-Based Autonomous Star Identification Algorithm

Most of the existing autonomous star identification algorithms use direct-match algorithms that prestore the star feature vectors in a database. During recognition, the measurements are compared with the reference feature vectors in sequence or by using binary-tree search. The computation time for the star recognition with a traditional model-based system is high, and it increases as the number of the feature patterns in the database increase. We propose an autonomous star identification algorithm using fuzzy neural logic networks. This is a parallel star identification algorithm with fast training speed. The simulation results based on the SKY2000 star catalog show that the proposed system can achieve both high recognition accuracy and fast recognition speed. Errors due to star magnitude measurement imprecision can also be minimized. (Author)