On Autocovariance Least-Squares Method for Noise Covariance Matrices Estimation

被引：42

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

页码：967 / 972

页数：6

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