Adaptive Tuning of the Unscented Kalman Filter for Satellite Attitude Estimation
Publication: Journal of Aerospace Engineering
Volume 28, Issue 3
Abstract
Determining the process noise covariance of the unscented Kalman filter (UKF) is a difficult procedure. The analytical approximation method gives satisfactory results in certain cases, but it fails when generalized for the estimation of the extended states, such as the case that sensor biases or scale factors are included in the state vector. The main aim of this research is to find an appropriate tuning algorithm for the process noise covariance of the UKF when the magnetometer biases are estimated, as well as attitude and gyro biases. In this sense, an adaptive tuning method for an UKF that is used for satellite attitude estimation is given and the adaptive UKF algorithm is tested in various scenarios for the attitude and sensor bias estimation. The given adaptation method is an easy way of tuning the filter, especially in the absence of any analytical approximation for the calculation of the process noise covariance, and the performed simulations show that by using the adaptive UKF, it is possible to get accurate estimates that are close to optimal.
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Acknowledgments
This work was supported in part by the Japanese government with the MONBUKAGAKUSHO scholarship, and also by the Japan Aerospace Exploration Agency (JAXA) with a research grant.
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Published In
Journal of Aerospace Engineering
Volume 28 • Issue 3 • May 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Dec 17, 2012
Accepted: Feb 5, 2014
Published online: Feb 7, 2014
Discussion open until: Dec 22, 2014
Published in print: May 1, 2015
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