EURASIP Journal on Applied Signal Processing 
Volume 2004 (2004), Issue 15, Pages 2278-2294
doi:10.1155/S1110865704406039

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

Joaquín Míguez,1 Mónica F. Bugallo,2 and Petar M. Djurić2

 1Departamento de Electrónica e Sistemas, Universidade da Coruña, Facultade de Informática, Campus de Elviña s/n, Coruña 15071 A, Spain
2Department of Electrical and Computer Engineering, State University of New York at Stony Brook, Stony Brook 11794-2350, NY, USA
Received 4 May 2003; Revised 29 January 2004

Abstract

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 2-dimensional space.