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 language | English (US) |
|---|---|
| Pages (from-to) | 1317-1336 |
| Number of pages | 20 |
| Journal | Advances in the Astronautical Sciences |
| Volume | 114 II |
| State | Published - Dec 1 2003 |
| Externally published | Yes |
| Event | Spaceflight Mechanics 2003: Proceedings of the AAS/AIAA Space Flight Mechanics Meeting - Ponce, Puerto Rico Duration: Feb 9 2003 → Feb 13 2003 |
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ASJC Scopus subject areas
- Aerospace Engineering
- Space and Planetary Science
Cite this
Autonomous artificial neural network star tracker for spacecraft attitude determination. / Trask, Aaron J.; Coverstone, Victoria.
In: Advances in the Astronautical Sciences, Vol. 114 II, 01.12.2003, p. 1317-1336.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Autonomous artificial neural network star tracker for spacecraft attitude determination
AU - Trask, Aaron J.
AU - Coverstone, Victoria
PY - 2003/12/1
Y1 - 2003/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=1542359677&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=1542359677&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:1542359677
VL - 114 II
SP - 1317
EP - 1336
JO - Advances in the Astronautical Sciences
JF - Advances in the Astronautical Sciences
SN - 1081-6003
ER -