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Resilient Minimum Entropy Filter Design for Non-Gaussian Stochastic Systems
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191,China
Control System Center, The University of Manchester, Manchester, M13 9PL, UK
* Author to whom correspondence should be addressed.
Received: 1 March 2013; in revised form: 27 March 2013 / Accepted: 2 April 2013 / Published: 10 April 2013
Abstract: In this paper, the resilient minimum entropy filter problem is investigated for the stochastic systems with non-Gaussian disturbances. The goal of designing the filter is to guarantee that the entropy of the estimation error is monotonically decreasing, moreover, the error system is exponentially ultimately bounded in the mean square. Based on the entropy performance function, a filter gain updating algorithm is presented to make the entropy decrease at every sampling instant k. Then the boundedness of the gain updating law is analyzed using the kernel density estimation technique. Furthermore, a suboptimal resilient filter gain is designed in terms of LMI. Finally, a simulation example is given to show the effectiveness of the proposed results.
Keywords: entropy decreasing; non-Gaussian systems; stochastic filtering; resilient filter gain; stochastic stability
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Wang, Y.; Wang, H.; Guo, L. Resilient Minimum Entropy Filter Design for Non-Gaussian Stochastic Systems. Entropy 2013, 15, 1311-1323.
Wang Y, Wang H, Guo L. Resilient Minimum Entropy Filter Design for Non-Gaussian Stochastic Systems. Entropy. 2013; 15(4):1311-1323.
Wang, Yan; Wang, Hong; Guo, Lei. 2013. "Resilient Minimum Entropy Filter Design for Non-Gaussian Stochastic Systems." Entropy 15, no. 4: 1311-1323.