Nonlinear Kalman filtering using fuzzy local linear models

Author

McGinnity, Shaun ; Irwin, George

Author_Institution

Dept. of Electr. & Electron. Eng., Queen´´s Univ., Belfast, UK

Volume

5

fYear

1997

fDate

4-6 Jun 1997

Firstpage

3299

Abstract

A local linear modelling based approach to nonlinear state estimation is introduced. The local models are defined using the Sugeno fuzzy inference framework and constructed using neurofuzzy modelling techniques. Two new fuzzy Kalman filters, which do not require further linearisation nor analytical system equations, are derived. Simulation results presented for a highly nonlinear target tracking problem suggest potential improvements when compared with conventional extended Kalman filtering

Keywords

Kalman filters; filtering theory; fuzzy logic; fuzzy neural nets; inference mechanisms; modelling; nonlinear filters; state estimation; Sugeno fuzzy inference framework; extended Kalman filtering; fuzzy local linear models; highly nonlinear target tracking problem; neurofuzzy modelling techniques; nonlinear Kalman filtering; nonlinear state estimation; Control engineering; Electric variables measurement; Filtering; Fuzzy systems; Kalman filters; Logic; Nonlinear equations; Nonlinear filters; State estimation; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.612075

Filename

612075