Title: Comparative study of Extended Kalman Filter, Linearised Kalman Filter and Particle Filter applied to low-cost GPS-based hybrid positioning system for land vehicles

Authors: M.A. Zamora-Izquierdo, D.F. Betaille, F. Peyret, C. Joly

Addresses: Metrology and Instrumentation Division, Laboratoire Central des Ponts et Chaussees – Centre de Nantes, 44341 Bouguenais Cedex, Nantes, France. ' Metrology and Instrumentation Division, Laboratoire Central des Ponts et Chaussees – Centre de Nantes, 44341 Bouguenais Cedex, Nantes, France. ' Metrology and Instrumentation Division, Laboratoire Central des Ponts et Chaussees – Centre de Nantes, 44341 Bouguenais Cedex, Nantes, France. ' MMetrology and Instrumentation Division, Laboratoire Central des Ponts et Chaussees – Centre de Nantes, 44341 Bouguenais Cedex, Nantes, France

Abstract: International research is very active in the topic of data fusion between GNSS and proprioceptive sensors to improve basic GNSS performances for advanced location-based aiding systems. In this frame, recursive Bayesian estimation methods, still are the most efficient and the most popular tools for measurement data fusion. This paper is to present comparisons, on the one hand between two very popular forms of the Kalman Filter: the so-called Linearized Kalman Filter (LKF), and the Extended Kalman Filter (EKF), and on the other hand between the Kalman Filter and one of its most promising challengers: the Particle Filter (PF). Experimental tests performed in two different circuits and discussion about comparative results are presented.

Keywords: intelligent transportation systems; ITS; data fusion; integrity; Kalman filtering; extended Kalman filter; linearised Kalman filter; particle filter; land vehicles; differential GPS; global positioning systems; GNSS; global navigation satellite systems; low-cost positioning systems.

DOI: 10.1504/IJIIDS.2008.018252

International Journal of Intelligent Information and Database Systems, 2008 Vol.2 No.2, pp.149 - 166

Published online: 13 May 2008 *

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