Most of the existing autonomous star identi 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 identi cation algorithm using fuzzy neural logic networks. This is a parallel star identi 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.
[1]
Ah-Hwee Tan,et al.
Adaptive resonance associative map
,
1995,
Neural Networks.
[2]
Stephen Grossberg,et al.
Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps
,
1992,
IEEE Trans. Neural Networks.
[3]
Stefan Wermter,et al.
Interactive Spoken-Language Processing in a Hybrid Connectionist System
,
1996,
Computer.
[4]
Robert H. Tolson,et al.
Onboard star identification without a priori attitude information
,
1995
.
[5]
Ah-Hwee Tan,et al.
Inductive neural logic network and the SCM algorithm
,
1997,
Neurocomputing.
[6]
H. F. Durrant-Whyte,et al.
A fast autonomous star-acquisition algorithm for spacecraft
,
1996
.
[7]
Nasser M. Nasrabadi,et al.
Object recognition using multilayer Hopfield neural network
,
1997,
IEEE Trans. Image Process..