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Variance-Constrained {cal H}_{\infty } Filtering for a Class of Nonlinear Time-Varying Systems With Multiple Missing Measurements: The Finite-Horizon Case

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4 Author(s)
Hongli Dong ; Space Control & Inertial Technol. Res. Center, Harbin Inst. of Technol., Harbin, China ; Zidong Wang ; Ho, D.W.C. ; Huijun Gao

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 . 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.

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Signal Processing, IEEE Transactions on  (Volume:58 ,  Issue: 5 )