Nontraditional Attitude Filtering with Simultaneous Process and Measurement Covariance Adaptation
Publication: Journal of Aerospace Engineering
Volume 32, Issue 5
Abstract
This study discusses simultaneous adaptation of the process and measurement noise covariance matrixes for a nontraditional attitude filtering algorithm. The nontraditional attitude filtering algorithm integrates the singular value decomposition (SVD) method with the unscented Kalman filter (UKF) to estimate the attitude of a nanosatellite. The SVD method uses magnetometer and Sun sensor measurements as the first stage of the algorithm and estimates the attitude of the nanosatellite, giving one estimate at a single frame. Then these estimated attitude terms are used as input to an adaptive UKF. The conventional UKF and the proposed adaptive UKF were compared with demonstrations of the attitude and attitude rate estimation of the satellite. Specifically, the Q (process noise covariance)-adaptation method is proposed. In the case of process noise increment, which may be caused by the changes in the environment or satellite dynamics, the performance of the Q-adaptive UKF was investigated.
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Acknowledgments
D. Cilden-Guler is supported by ASELSAN (Military Electronic Industries) and TUBITAK (Scientific and Technological Research Council of Turkey) Ph.D. Scholarships.
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Published In
Journal of Aerospace Engineering
Volume 32 • Issue 5 • September 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jul 27, 2018
Accepted: Jan 29, 2019
Published online: May 17, 2019
Published in print: Sep 1, 2019
Discussion open until: Oct 17, 2019
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