Journal
APPLIED MATHEMATICS LETTERS
Volume 26, Issue 1, Pages 80-86Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.aml.2012.03.036
Keywords
Sensor failure; Kalman filtering; Robust finite-horizon filtering
Categories
Funding
- Natural Science Foundation of China [61102007, 60974147, 61032001]
- Fundamental Research Funds for the Central Universities
- Southwest University for Nationalities [82000337]
Ask authors/readers for more resources
This work deals with the filtering problem for norm-bounded uncertain discrete dynamic systems with multiple sensors having different stochastic failure rates. For tackling the uncertainties of the covariance matrices of state and state estimation error simultaneously, their upper bounds containing a scaling parameter are derived, and then a robust finite-horizon filtering minimizing the upper bound of the estimation error covariance is proposed. Furthermore, an optimal scaling parameter is exploited to reduce the conservativeness of the upper bounds of the state and estimation error covariances, which leads to an optimal robust filtering for all possible missing measurements and all admissible parameter uncertainties. A numerical example illustrates the performance improvement over the traditional Kalman filtering method. (C) 2012 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available