Estimation of noise covariance matrices for periodic systems

被引：8
|
作者：
Simandl, Miroslav [1 ]
Dunik, Jindrich [1 ]
机构：
[1] Univ W Bohemia, Dept Cybernet, Fac Sci Appl, Univ 8, Plzen 30614, Czech Republic
来源：
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING | 2011年 / 25卷 / 10期
关键词：
stochastic systems; periodic systems; state estimation; estimation theory; noise covariance matrices estimation; LEAST-SQUARES METHOD; IDENTIFICATION;
D O I：
10.1002/acs.1255
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Estimation of the noise covariance matrices for linear time-variant stochastic dynamic periodic systems is treated. The novel offline method for estimation of the covariance matrices of the state and measurement noises is designed. The method is based on analysis of second-order statistics of the state estimate produced by the linear multi-step predictor. The estimates of the noise covariance matrices are unbiased and converge to the true values with increasing number of data. The theoretical results are illustrated in numerical examples. Copyright (C) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码：928 / 942
页数：15
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