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Erschienen in:
2013 | OriginalPaper | Buchkapitel
25. Centralized Fusion Steady-State Robust Kalman Filter for Uncertain Multisensor Systems
verfasst von : Peng Zhang, Wenjuan Qi, Zili Deng
Erschienen in: Proceedings of 2013 Chinese Intelligent Automation Conference
Verlag: Springer Berlin Heidelberg
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Abstract
For multisensor systems with uncertain noise variances, a centralized fusion steady-state robust Kalman filter with the upper bound of noise variances is presented. Based on the Lyapunov equation, it is proved that the local and centralized fusion steady-state Kalman filter are robust, i.e., the variances of the actual filtering errors are less than conservative variances of the corresponding robust Kalman filters. Further, it is proved that the robust accuracy of the centralized fusion robust Kalman filter is higher than that of the local robust Kalman filter. A Monte-Carlo simulation example shows its correctness.