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

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

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-dimensional space.

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Correspondence to Joaquín Míguez.

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An erratum to this article is available at http://dx.doi.org/10.1155/ASP/2006/78708.

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Míguez, J., Bugallo, M.F. & Djurić, P.M. A New Class of Particle Filters for Random Dynamic Systems with Unknown Statistics. EURASIP J. Adv. Signal Process. 2004, 303619 (2004). https://doi.org/10.1155/S1110865704406039

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Keywords and phrases

  • particle filtering
  • dynamic systems
  • online estimation
  • stochastic optimization