Neural-Network-Based Autonomous Star Identification Algorithm

Most of the existing autonomous star identiŽ cation 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 recognitionwith 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 identiŽ cation algorithm using fuzzy neural logic networks. This is a parallel star identiŽ cation algorithm with fast training speed. The simulation results based on the SKY2000 star catalog (Myers, J. R., Sande, C. B., Miller, A. C., Warren, W. H., and Tracewell, D. A., “The SKY2000 Master Star Catalog,”AAS/AIAA SpaceMechanics Symposium, AAS, SanDiego, 1997,pp. 1–16) show that the proposed system can achieve both high recognition accuracy and fast recognition speed. Errors due to starmagnitudemeasurement imprecision can also be minimized.