On Autocovariance Least-Squares Method for Noise Covariance Matrices Estimation

被引：44
作者：
Dunik, Jindrich [1 ]
Straka, Ondrej [1 ]
Simandl, Miroslav [1 ]
机构：
[1] Univ West Bohemia, Fac Sci Appl, Dept Cybernet, Plzen 30614, Czech Republic
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2017年 / 62卷 / 02期
关键词：
Covariance estimation; Kalman filter; state estimation; LINEAR-SYSTEMS; IDENTIFICATION; STATISTICS;
D O I：
10.1109/TAC.2016.2571899
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 technical note focuses on the estimation of the noise covariance matrices of the state space models. Stress is laid on the autocovariance least-squares method providing unbiased estimates of the noise covariance matrices of linear systems. In particular, two topics are discussed; first, selection of the predictor gain as a key parameter of the method, second, generalization of the method for linear systems with a time-varying measurement equation. The theoretical results are illustrated in numerical examples.
引用
收藏
页码：967 / 972
页数：6
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