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|Title:||Robust filtering for a class of nonlinear stochastic systems with probability constraints|
|Keywords:||Probability constraint;Time-varying systems;Measurements degradation;Stochastic nonlinearity;Parameter uncertainty|
|Citation:||Automation and Remote Control, 77(1): pp. 37 - 54, (2016)|
|Abstract:||This paper is concerned with the probability-constrained filtering problem for a class of time-varying nonlinear stochastic systems with estimation error variance constraint. The stochastic nonlinearity considered is quite general that is capable of describing several well-studied stochastic nonlinear systems. The second-order statistics of the noise sequence are unknown but belong to certain known convex set. The purpose of this paper is to design a filter guaranteeing a minimized upper-bound on the estimation error variance. The existence condition for the desired filter is established, in terms of the feasibility of a set of difference Riccati-like equations, which can be solved forward in time. Then, under the probability constraints, a minimax estimation problem is proposed for determining the suboptimal filter structure that minimizes the worst-case performance on the estimation error variance with respect to the uncertain second-order statistics. Finally, a numerical example is presented to show the effectiveness and applicability of the proposed method.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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