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Proceedings Paper

Neural network approach to star-field recognition
Author(s): Maximillian J. Domeika; Edward W. Page; Gene A. Tagliarini
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Paper Abstract

Automatic recognition of stellar fields viewed by an imaging camera has numerous applications ranging from spacecraft navigation to pointing of spaceborne instruments. The usual approach to recognition is to develop an efficient algorithm for matching stars identified in the imager's field of view with star data recorded in an onboard catalog. Matching stars within a field of view with corresponding stars stored in a catalog requires finding a subset of the stars in the catalog that have positions and magnitudes that match those of the stars in the field of view. This paper presents a neural network approach to the problem of star field recognition. A Hopfield neural network is used to find a subset of the stars in the catalog that provides a good match to stars in the imager's field of view. The matching process employs a compatibility function, similar to a fuzzy membership function, to grade the similarity between stars in the field of view and those in the catalog.

Paper Details

Date Published: 6 April 1995
PDF: 9 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205097
Show Author Affiliations
Maximillian J. Domeika, Clemson Univ. (United States)
Edward W. Page, Clemson Univ. (United States)
Gene A. Tagliarini, Clemson Univ. (United States)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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