A generalized autocovariance least-squares method for Kalman filter tuning

This paper discusses a method for estimating noise covariances from process data. In linear stochastic state-space representations the true noise covariances are generally unknown in practical applications. Using estimated covariances a Kalman filter can be tuned in order to increase the accuracy of...

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Bibliographic Details
Published in:Journal of process control 2008-08, Vol.18 (7), p.769-779
Main Authors: Åkesson, Bernt M., Jørgensen, John Bagterp, Poulsen, Niels Kjølstad, Jørgensen, Sten Bay
Format: Article
Language:English
Subjects:
Covariance estimation
Kalman filtering
Optimal estimation
Semidefinite programming
State estimation
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Summary:This paper discusses a method for estimating noise covariances from process data. In linear stochastic state-space representations the true noise covariances are generally unknown in practical applications. Using estimated covariances a Kalman filter can be tuned in order to increase the accuracy of the state estimates. There is a linear relationship between covariances and autocovariance. Therefore, the covariance estimation problem can be stated as a least-squares problem, which can be solved as a symmetric semidefinite least-squares problem. This problem is convex and can be solved efficiently by interior-point methods. A numerical algorithm for solving the symmetric is able to handle systems with mutually correlated process noise and measurement noise.
ISSN:0959-1524
1873-2771
DOI:10.1016/j.jprocont.2007.11.003