Extended Kalman filtering for fuzzy modelling and multi-sensor fusion

Rigatos, G; Tzafestas, S

dc.contributor.author	Rigatos, G	en
dc.contributor.author	Tzafestas, S	en
dc.date.accessioned	2014-03-01T01:26:20Z
dc.date.available	2014-03-01T01:26:20Z
dc.date.issued	2007	en
dc.identifier.issn	1387-3954	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18023
dc.subject	Extended Kalman filtering	en
dc.subject	Fuzzy modelling	en
dc.subject	Gauss-Newton method	en
dc.subject	Multi-sensor fusion	en
dc.subject	Vehicle localization	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Mathematics, Applied	en
dc.subject.other	EXPERIMENTAL VALIDATION	en
dc.title	Extended Kalman filtering for fuzzy modelling and multi-sensor fusion	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/01443610500212468	en
heal.identifier.secondary	http://dx.doi.org/10.1080/01443610500212468	en
heal.language	English	en
heal.publicationDate	2007	en
heal.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. rst 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.	en
heal.publisher	TAYLOR & FRANCIS INC	en
heal.journalName	Mathematical and Computer Modelling of Dynamical Systems	en
dc.identifier.doi	10.1080/01443610500212468	en
dc.identifier.isi	ISI:000248007800003	en
dc.identifier.volume	13	en
dc.identifier.issue	3	en
dc.identifier.spage	251	en
dc.identifier.epage	266	en