期刊
INTERNATIONAL JOURNAL OF GENERAL SYSTEMS
卷 44, 期 2, 页码 142-154出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/03081079.2014.973728
关键词
optimal least-squares estimation; innovation approach; randomly delayed observations; random parameter matrices; correlated noises
资金
- Ministerio de Ciencia e Innovacion [MTM2011-24718]
In this paper, the optimal least-squares state estimation problem is addressed for a class of discrete-time multisensor linear stochastic systems with state transition and measurement random parameter matrices and correlated noises. It is assumed that at any sampling time, as a consequence of possible failures during the transmission process, one-step delays with different delay characteristics may occur randomly in the received measurements. The random delay phenomenon is modelled by using a different sequence of Bernoulli random variables in each sensor. The process noise and all the sensor measurement noises are one-step autocorrelated and different sensor noises are one-step cross-correlated. Also, the process noise and each sensor measurement noise are two-step cross-correlated. Based on the proposed model and using an innovation approach, the optimal linear filter is designed by a recursive algorithm which is very simple computationally and suitable for online applications. A numerical simulation is exploited to illustrate the feasibility of the proposed filtering algorithm.
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