Autonomous artificial neural network star tracker for spacecraft attitude determination

Aaron J. Trask, Victoria L. Coverstone

Research output: Contribution to journalArticlepeer-review

Abstract

An artificial neural network based autonomous star tracker prototype for precise spacecraft attitude determination is developed. A new technique of star pattern encoding that removes the star magnitude dependency is presented. The convex hull technique is developed in which the stars in the field of view are treated as a set of points. The convex hull of these points is found and stored as line segments and interior angles moving clockwise from the shortest segment. This technique does not depend on star magnitudes and allows a varying number of stars to be identified and used in calculating the attitude quaternion. This technique combined with feed-forward neural network pattern identification creates a robust and fast technique for solving the "lost-in-space" problem. Night sky testing is used to validate a system consisting of a charged-coupled-device-based camera head unit and integrated control hardware and software. The artificial neural network star pattern match algorithm utilizes a sub catalog of the SKY2000 star catalog. The experimental results are real time comparisons of the star tracker observed motion with the rotational motion of the Earth. The time required to solve the "lost-in-space" problem for this star tracker prototype is on average 9.5 seconds.

Original languageEnglish (US)
Pages (from-to)1315-1335
Number of pages21
JournalAdvances in the Astronautical Sciences
Volume114
Issue numberSUPPL.
StatePublished - Dec 1 2003
Externally publishedYes

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science

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