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Proceedings Paper

Enhancing attitude estimation accuracy via system noise optimization
Author(s): Quang M. Lam; Johnathan Lakso; Teresa Hunt; Peter Vanderham
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Paper Abstract

It is well known to the Kalman filter design and estimation community that the values for the process noise, Q, and measurement noise, R, covariance matrices primarily dictate the filter performance. In addition, selecting proper values for Q and R is traditionally done in an ad-hoc manner. This paper provides a new look into the roles of the process noise and measurement noise matrices using the spacecraft attitude estimation problem as the design benchmark. This includes an interesting situation where the theoretical values of Q and R, derived as a function of gyro and star tracker noise parameters, are exactly matched with the noise characteristics employed on the sensor model side. However, the filter still exhibits poor attitude estimation performance, as measured against an attitude knowledge requirement, while subject to a high rate slew profile. A simulation based tuning methodology is developed to optimize the filter performance and bring the attitude estimation back to within the required attitude knowledge bound.

Paper Details

Date Published: 15 September 2004
PDF: 12 pages
Proc. SPIE 5403, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III, (15 September 2004); doi: 10.1117/12.540925
Show Author Affiliations
Quang M. Lam, Swales Aerospace (United States)
Johnathan Lakso, Swales Aerospace (United States)
Teresa Hunt, Swales Aerospace (United States)
Peter Vanderham, Swales Aerospace (United States)


Published in SPIE Proceedings Vol. 5403:
Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III
Edward M. Carapezza, Editor(s)

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