Hybrid Kalman filter-fuzzy logic adaptive multisensor data fusion architectures

Author

Escamilla-Ambrosio, P. Jorge ; Mort, Neil

Author_Institution

Dept. of Aerosp. Eng., Bristol Univ., UK

Volume

5

fYear

2003

fDate

9-12 Dec. 2003

Firstpage

5215

Abstract

In this work the recently developed fuzzy logic-based adaptive Kalman filter (FL-AKF) is used to build adaptive centralized, decentralized, and federated Kalman filters for adaptive multisensor data fusion (AMSDF). The adaptation carried out is in the sense of adaptively adjusting the measurement noise covariance matrix of each local FL-AKF to fit the actual statistics of the noise profiles present in the incoming measured data. A fuzzy inference system (FIS) based on a covariance-matching technique is used as the adaptation mechanism. The effectiveness and accuracy of the proposed AMSDF approaches is demonstrated in a simulated example.

Keywords

adaptive Kalman filters; covariance matrices; fuzzy logic; sensor fusion; statistics; adaptive centralized Kalman filters; adaptive decentralized Kalman filters; adaptive federated Kalman filters; adaptive multisensor data fusion; covariance matching technique; covariance matrix; fuzzy inference system; fuzzy logic based adaptive Kalman filter; measurement noise; statistics; Adaptive filters; Buildings; Computer architecture; Covariance matrix; Error correction; Filtering; Fuzzy logic; Kalman filters; Noise measurement; Time measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2003. Proceedings. 42nd IEEE Conference on

ISSN

0191-2216

Print_ISBN

0-7803-7924-1

Type

conf

DOI

10.1109/CDC.2003.1272465

Filename

1272465