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

  • Affiliations:

  • 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

  • Venue:
  • International Journal of Intelligent Information and Database Systems
  • Year:
  • 2008

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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.