Extended Kalman filtering for fuzzy modelling and multi-sensor fusion

作者：Gerasimos Rigatos, Spyros G. Tzafestaş · 发表于：Mathematical and Computer Modelling of Dynamical Systems · 年份：2007 · DOI：10.1080/01443610500212468 · 被引用次数：338 · 研究领域：Robotics and Sensor-Based Localization、Target Tracking and Data Fusion in Sensor Networks、Fuzzy Logic and Control Systems

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.