Comparative study of Extended Kalman Filter, Linearised Kalman Filter and Particle Filter applied to low-cost GPS-based hybrid positioning system for land vehicles. (15th May 2008)

• Record Type: Journal Article
• Authors: Zamora-Izquierdo, M.A.; Betaille, D.F.; Peyret, F.; Joly, C.
• Is Part Of: International journal of intelligent information and database systems. Volume 2:Number 2(2008)
• Page Start: 149
• Page End: 166
• Publication Date: 2008-05-15
• Subjects: intelligent transportation systems -- ITS -- data fusion -- integrity -- Kalman &amp -- #64257 -- ltering -- extended Kalman filter -- linearised Kalman filter -- particle filter -- land vehicles -- differential GPS -- global positioning systems -- GNSS -- global navigation satellite systems -- low-cost positioning systems; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

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.