Online adaptation of the number of particles of SMC methods

Victor Elvira, Joaquín Míguez, Petar M. Djurić

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Particle filtering is a widely used sequential methodology that approximates probability distributions by using discrete random measures composed of weighted particles. A large number of particles improves the quality of the approximation but increases the computational requirements. Although there exists an abundant variety of particle filtering algorithms in the literature, there is lack of work devoted to selecting or adapting the number of particles systematically. In this paper we propose a novel methodology for online assessment of convergence of particle filtering. Based on theoretical analysis of the assessment, we propose an algorithm for the adaptation of the number of particles in online manner. The performance of the proposed algorithm is demonstrated for two state-space models.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4378-4382
Number of pages5
Volume2016-May
ISBN (Electronic)9781479999880
DOIs
Publication statusPublished - 18 May 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period20/03/1625/03/16

Keywords

  • adaptive complexity
  • computational complexity
  • convergence analysis
  • convergence assessment
  • Particle filtering
  • predictive distribution
  • sequential Monte Carlo

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