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22 March 1996 Adaptive resonance theory 2 neural network approach to star field recognition
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Abstract
Automatic recognition of star fields viewed by an imaging camera has numerous applications ranging from spacecraft navigation to pointing spaceborne instruments. The usual approach is to develop an efficient algorithm for matching stars in the imager's field of view with star data recorded in an on-board catalog. The matching process requires finding a subset of the stars in the catalog that have positions and magnitudes corresponding to those of the stars in the field of view. This paper presents an Adaptive Resonance Theory 2 (ART 2) approach to the problem of star field recognition. An ART 2 neural network is used to find a subset of stars in the catalog that provides a good match to stars in the imager's field of view. A method is presented which makes training the network unnecessary because the connection weights between the neurons are prescribed.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Maximillian J. Domeika, Charles W. Roberson, Edward W. Page, and Gene A. Tagliarini "Adaptive resonance theory 2 neural network approach to star field recognition", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235948
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