Design of measurement difference autocovariance method for estimation of process and measurement noise covariances

被引:30
作者
Dunik, Jindrich [1 ,2 ]
Kost, Oliver [1 ,2 ]
Straka, Ondrej [1 ,2 ]
机构
[1] Univ West Bohemia, Fac Sci Appl, Dept Cybernet, Univ 8, Plzen 30614, Czech Republic
[2] Univ West Bohemia, Fac Sci Appl, European Ctr Excellence NTIS, Univ 8, Plzen 30614, Czech Republic
关键词
Identification; State-space models; State estimation; Prediction; Noise covariance matrices; LEAST-SQUARES METHOD; KALMAN FILTER; IDENTIFICATION; VARIANCES; MATRICES; MODELS;
D O I
10.1016/j.automatica.2017.12.040
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The paper deals with the estimation of the process and measurement noise covariance matrices of a system described by the linear time-varying state-space model. In particular, the stress is laid on the correlation methods and a novel method, the measurement difference autocovariance method, is designed. The proposed method is based on the statistical analysis of an augmented measurement prediction error leading to a system of linear matrix equations for the elements of the noise covariance matrices. Compared to other correlation methods, the proposed method provides unbiased estimates even for a finite number of measurements. The theoretical results are discussed and illustrated in a numerical example. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:16 / 24
页数:9
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