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Title: Robust variance-constrained filtering for a class of nonlinear stochastic systems with missing measurements
Authors: Ma, L
Wang, Z
Hu, J
Bo, S
Guo, Z
Keywords: Nonlinear systems
Stochastic systems
Robust filtering
Variance constraints
Missing measurements
Publication Date: 2010
Publisher: Elsevier
Citation: Signal Processing, 90(6): 2060-2071, Jun 2010
Abstract: This paper is concerned with the robust filtering problem for a class of nonlinear stochastic systems with missing measurements and parameter uncertainties. The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution, and the nonlinearities are expressed by the statistical means. The purpose of the filtering problem is to design a filter such that, for all admissible uncertainties and possible measurements missing, the dynamics of the filtering error is exponentially mean-square stable, and the individual steady-state error variance is not more than prescribed upper bound. A sufficient condition for the exponential mean-square stability of the filtering error system is first derived and an upper bound of the state estimation error variance is then obtained. In terms of certain linear matrix inequalities (LMIs), the solvability of the addressed problem is discussed and the explicit expression of the desired filters is also parameterized. Finally, a simulation example is provided to demonstrate the effectiveness and applicability of the proposed design approach.
Description: The official published version of the article can be found at the link below.
Sponsorship: This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK and the Alexander von Humboldt Foundation of Germany.
ISSN: 0165-1684
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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