Variance-constrained H∞ filtering for a class of nonlinear time-varying systems with multiple missing measurements: the finite-horizon case

Authors:
Hongli Dong;Zidong Wang;Daniel W. C. Ho;Huijun Gao
Affiliations:
Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, China and College of Electrical and Information Engineering, Daqing Petroleum Institute, Daqing, Chin ...;Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UK;Department of Mathematics, City University of Hong Kong, Kowloon, Hong Kong;Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, China
Venue:
IEEE Transactions on Signal Processing
Year:
2010

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Abstract

This paper is concerned with the robust H∞ finite-horizon filtering problem for a class of uncertain nonlinear discrete time-varying stochastic systems with multiple missing measurements and error variance constraints. All the system parameters are time-varying and the uncertainty enters into the state matrix. The measurement missing phenomenon occurs in a random way, and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution in the interval [0 1]. The stochastic nonlinearities under consideration here are described by statistical means which can cover several classes of well-studied nonlinearities. Sufficient conditions are derived for a finite-horizon filter to satisfy both the estimation error variance constraints and the prescribed H∞ performance requirement. These conditions are expressed in terms of the feasibility of a series of recursive linear matrix inequalities (RLMIs). Simulation results demonstrate the effectiveness of the developed filter design scheme.