Modified extended Kalman filtering and a real-time parallel algorithm for system parameter identification

Author

Chui, Charles K. ; Chen, Guanrong ; Chui, Herman C.

Author_Institution

Dept. of Math., Texas A&M Univ., College Station, TX, USA

Volume

35

Issue

1

fYear

1990

fDate

1/1/1990 12:00:00 AM

Firstpage

100

Lastpage

104

Abstract

A modification of the extended Kalman filter (EKF) algorithm, which is called MEKF for short, it introduced. The modification is achieved by an improved linearization procedure. For this purpose, a parallel computational scheme is recommended, and it has immediate applications to identifying unknown system parameters of time-varying, linear, stochastic state-space models in real time. It should be noted that just like the EKF, the MEKF is also ad hoc in the sense that it is a real-time approximation method. Numerical examples with computer simulation are included to demonstrate the effectiveness of this procedure as compared to the EKF algorithm

Keywords

Kalman filters; parallel algorithms; parameter estimation; computer simulation; linearization; modified extended Kalman filtering; parameter estimation; parameter identification; real-time parallel algorithm; time-varying linear stochastic state-space models; Automatic control; Control system synthesis; Control systems; Convergence; Filtering; Kalman filters; Parallel algorithms; Real time systems; Stochastic systems; Upper bound;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.45155

Filename

45155