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Proceedings Paper

Method for star identification using neural networks
Author(s): Clark S. Lindsey; Thomas Lindblad; Age J. Eide
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Paper Abstract

Identification of star constellations with an onboard star tracker provides the highest precision of all attitude determination techniques for spacecraft. A method for identification of star constellations inspired by neural network (NNW) techniques is presented. It compares feature vectors derived from histograms of distances to multiple stars around the unknown star. The NNW method appears most robust with respect to position noise and would require a smaller database than conventional methods, especially for small fields of view. The neural network method is quite slow when performed on a sequential (serial) processor, but would provide very high speed if implemented in special hardware. Such hardware solutions could also yield lower low weight and low power consumption, both important features for small satellites.

Paper Details

Date Published: 4 April 1997
PDF: 8 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271545
Show Author Affiliations
Clark S. Lindsey, Royal Institute of Technology (Sweden)
Thomas Lindblad, Royal Institute of Technology (Sweden)
Age J. Eide, Ostfold College (Norway)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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