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Mathematical and Computer Modelling of Dynamical Systems
Methods, Tools and Applications in Engineering and Related Sciences
Volume 13, 2007 - Issue 3
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Original Articles

Extended Kalman filtering for fuzzy modelling and multi-sensor fusion

G. Rigatos Department of Electrical and Computer Engineering, National Technical University of Athens, Zografu Campus, 15773, Athens, GreeceCorrespondence[email protected]
&
S. Tzafestas Department of Electrical and Computer Engineering, National Technical University of Athens, Zografu Campus, 15773, Athens, Greece
Pages 251-266 | Published online: 15 May 2007
 
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Abstract

Extended Kalman Filtering (EKF) is proposed for: (i) the extraction of a fuzzy model from numerical data; and (ii) the localization of an autonomous vehicle. In the first case, the EKF algorithm is compared to the Gauss–Newton nonlinear least-squares method and is shown to be faster. An analysis of the EKF convergence is given. In the second case, the EKF algorithm estimates the state vector of the autonomous vehicle by fusing data coming from odometric sensors and sonars. Simulation tests show that the accuracy of the EKF-based vehicle localization is satisfactory.

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