Variance reduction for particle filters of systems with time scale separation

Dror Givon, Panagiotis Stinis, Jonathan Weare

Research output: Contribution to journalArticlepeer-review


We present a particle filter construction for a system that exhibits time-scale separation. The separation of time scales allows two simplifications that we exploit: 1) the use of the averaging principle for the dimensional reduction of the dynamics for each particle during the prediction step and 2) the factorization of the transition probability for the Rao-Blackwellization of the update step. The resulting particle filter is faster and has smaller variance than the particle filter based on the original system. The method is tested on a multiscale stochastic differential equation and on a multiscale pure jump diffusion motivated by chemical reactions.

Original languageEnglish (US)
Pages (from-to)424-435
Number of pages12
JournalIEEE Transactions on Signal Processing
Issue number2
StatePublished - 2009


  • Dimensional reduction
  • Jump Markov processes
  • Multiscale
  • Particle filter
  • Rao - Blackwellization
  • Stochastic differential equations
  • Variance reduction

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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