Simulation study demonstrates rapid fuzzy modeling and reliable localization in autonomous vehicles, highlighting extended Kalman filtering efficiency for multi-sensor data fusion.
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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Rigatos et al. (2007) studied this question.
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