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. © (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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