Modified LOLIMOT algorithm for nonlinear centralized Kalman filtering fusion

Author

Rezaie, Javad ; Moshiri, Behzad ; Rafati, Amir ; Araabi, Babak N.

Author_Institution

Tehran Univ., Tehran

fYear

2007

fDate

9-12 July 2007

Firstpage

1

Lastpage

8

Abstract

In this paper, first an enhanced neuro-fuzzy method for modeling nonlinear system is presented In this method we use EM algorithm for identification of local models, which gain us model mismatch covariance. The achieved model can be stated in state space model as a linear time-varying system. As the noise and model mismatch covariance is known, Kalman filter can be easily used for centralized estimation fusion. The simulations show that using centralized estimation fusion will enhance the estimation accuracy to a great deal.

Keywords

Kalman filters; centralised control; expectation-maximisation algorithm; fuzzy control; linear systems; neurocontrollers; nonlinear systems; sensor fusion; state estimation; state-space methods; time-varying systems; trees (mathematics); expectation-maximisation algorithm; linear time-varying system; local linear model tree; model mismatch covariance; modified LOLIMOT algorithm; neuro-fuzzy method; nonlinear centralized Kalman filtering fusion; nonlinear system; state space model; Centralized control; Filtering algorithms; Frequency locked loops; Fuzzy sets; Intelligent control; Kalman filters; Least squares approximation; Process control; Space technology; State estimation; Centralized Kalman filtering; Expectation Maximization; Local Linear Model; Measurement fusion; NeuroFuzzy; Nonlinear state estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2007 10th International Conference on

Conference_Location

Quebec, Que.

Print_ISBN

978-0-662-45804-3

Electronic_ISBN

978-0-662-45804-3

Type

conf

DOI

10.1109/ICIF.2007.4408110

Filename

4408110