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Robust Variance Constrained Filter Design for Systems with Non-Gaussian Noises

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3 Author(s)
Fuwen Yang ; Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, United Kingdom. Email:, Telephone: 0044 1895 265439, Fax: 0044 1895 251686 ; Yongmin Li ; Xiaohui Liu

In this paper, a variance constrained filtering problem is considered for systems with both non-Gaussian noises and polytopic uncertainty. A novel filter is developed to estimate the systems states based on the current observation and known deterministic input signals. A free parameter is introduced in the filter to handle the uncertain input matrix in the known deterministic input term. In addition, unlike the existing variance constrained filters, which are constructed by the previous observation, the filter is formed from the current observation. A time-varying linear matrix inequality (LMI) approach is used to derive an upper bound of the state estimation error variance. The optimal bound is obtained by solving a convex optimisation problem via Semi-Definite Programming (SDP) approach. Simulation results are provided to demonstrate the effectiveness of the proposed method.

Published in:

Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on

Date of Conference:

6-8 April 2008