An Introduction to Twisted Particle Filters and Parameter Estimation in Non-linear State-space Models

Juha Ala-Luhtala, Nick Whiteley, Kari Heine, Robert Piche

Research output: Contribution to journalArticle (Academic Journal)peer-review

15 Citations (Scopus)
266 Downloads (Pure)


Twisted particle filters are a class of sequential Monte Carlo methods recently introduced by Whiteley and Lee to improve the efficiency of marginal likelihood estimation in state-space models. The purpose of this article is to
extend the twisted particle filtering methodology, establish accessible theoretical results which convey its rationale, and provide a demonstration of its practical performance within particle Markov chain Monte Carlo for estimating static model parameters. We derive twisted particle filters that incorporate systematic or multinomial resampling and information from historical particle states, and a transparent proof which identifies the optimal algorithm for marginal likelihood estimation. We demonstrate how to approximate the optimal algorithm for nonlinear state-space models with Gaussian noise and we apply such approximations to two examples: a range and bearing tracking problem and an indoor positioning problem with Bluetooth signal strength measurements. We demonstrate improvements over standard algorithms in terms of variance of marginal likelihood estimates and Markov chain autocorrelation for given CPU time, and improved tracking performance using estimated parameters.
Original languageEnglish
Pages (from-to)4875-4890
Number of pages6
JournalIEEE Transactions on Signal Processing
Issue number18
Early online date5 May 2016
Publication statusPublished - 15 Sep 2016


  • Particle filter
  • sequential Monte Carlo
  • particle MCMC
  • Gaussian state-space model
  • parameter estimation


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