Open Access

A New Class of Particle Filters for Random Dynamic Systems with Unknown Statistics

  • Joaquín Míguez1Email author,
  • Mónica F. Bugallo2 and
  • Petar M. Djurić2
EURASIP Journal on Advances in Signal Processing20042004:303619

Received: 4 May 2003

Published: 7 November 2004

The Research Article to this article has been published in EURASIP Journal on Advances in Signal Processing 2006 2006:078708


In recent years, particle filtering has become a powerful tool for tracking signals and time-varying parameters of random dynamic systems. These methods require a mathematical representation of the dynamics of the system evolution, together with assumptions of probabilistic models. In this paper, we present a new class of particle filtering methods that do not assume explicit mathematical forms of the probability distributions of the noise in the system. As a consequence, the proposed techniques are simpler, more robust, and more flexible than standard particle filters. Apart from the theoretical development of specific methods in the new class, we provide computer simulation results that demonstrate the performance of the algorithms in the problem of autonomous positioning of a vehicle in a -dimensional space.

Keywords and phrases

particle filteringdynamic systemsonline estimationstochastic optimization


Authors’ Affiliations

Departamento de Electrónica e Sistemas, Universidade da Coruña, Facultade de Informática, Coruña, Spain
Department of Electrical and Computer Engineering, State University of New York at Stony Brook, Stony Brook, USA


© Míguez et al. 2004