期刊
AUTOMATICA
卷 49, 期 11, 页码 3440-3448出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2013.08.021
关键词
Recursive Filtering; Random parameter matrix; Multiple fading measurements; Stochastic nonlinearity; Autocorrelation and cross-correlation
资金
- National 973 Project [2009CB320600]
- National Natural Science Foundation of China [61273156, 61273201, 61329301, 61333012, 11301118]
- State Key Laboratory of Integrated Automation for the Process Industry (Northeastern University) of China
- Engineering and Physical Sciences Research Council (EPSRC) of the U.K [GR/S27658/01]
- Royal Society of the U.K.
- Alexander von Humboldt Foundation of Germany
This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems with random parameter matrices, multiple fading measurements and correlated noises. The phenomenon of measurement fading occurs in a random way and the fading probability for each sensor is governed by an individual random variable obeying a certain probability distribution over the known interval [beta(k), gamma(k)]. Such a probability distribution could be any commonly used discrete distribution over the interval [beta(k), gamma(k)] that covers the Bernoulli distribution as a special case. The process noise and the measurement noise are one-step autocorrelated, respectively. The process noise and the measurement noise are two-step cross-correlated. The purpose of the addressed filtering problem is to design an unbiased and recursive filter for the random parameter matrices, stochastic nonlinearity, and multiple fading measurements as well as correlated noises. Intensive stochastic analysis is carried out to obtain the filter gain characterized by the solution to a recursive matrix equation. The proposed scheme is of a form suitable for recursive computation in online applications. A simulation example is given to illustrate the effectiveness of the proposed filter design scheme. (C) 2013 Elsevier Ltd. All rights reserved.
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