Research on the estimation to unknown noise statistics Q and R of nonlinear time-varying system

Author

CHEN, Rui ; Wei, Jinyu ; Zheng, Qingchun ; Bi, Ran ; Gu, Chengkui

Author_Institution

Sch. of Manage., Tianjin Univ. of Technol., Tianjin

fYear

2008

fDate

25-27 June 2008

Firstpage

8972

Lastpage

8974

Abstract

This paper presents a new method to estimate process noise covariances Q and observation noise covariances R of nonlinear time-varying system. Based on the analysis of key factor to estimate noise covariances Q and R for identification and prediction of nonlinear time-varying system using extended Kalman filter based on neural network. Then the paper extends the Mehra approach of noise statistics estimation of constant system to general nonlinear time-varying system based on covariances-matching techniques. The paper verified the effectiveness of the new method by identification and prediction results of two nonlinear time-varying systems at last.

Keywords

Kalman filters; estimation theory; nonlinear systems; time-varying systems; constant system; covariances-matching techniques; extended Kalman filter; general nonlinear time-varying system; neural network; noise statistics estimation; observation noise covariances; process noise covariances; unknown noise statistics; Automation; Engineering management; Intelligent control; Neural networks; Paper technology; Radio access networks; Statistics; Systems engineering and theory; Technology management; Time varying systems; covariances-matching techniques; estimation; noise covariances Q and R; nonlinear time-varying system;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4594348

Filename

4594348