Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models

Fredrik Lindsten, Pete Bunch, Simo Särkkä, Thomas B. Schön, Simon J. Godsill

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)

Abstract

Sequential Monte Carlo (SMC) methods, such as the particle filter, are by now one of the standard computational techniques for addressing the filtering problem in general state-space models. However, many applications require post-processing of data offline. In such scenarios the smoothing problem-in which all the available data is used to compute state estimates-is of central interest. We consider the smoothing problem for a class of conditionally linear Gaussian models. We present a forward-backward-type Rao-Blackwellized particle smoother (RBPS) that is able to exploit the tractable substructure present in these models. Akin to the well known Rao-Blackwellized particle filter, the proposed RBPS marginalizes out a conditionally tractable subset of state variables, effectively making use of SMC only for the "intractable part" of the model. Compared to existing RBPS, two key features of the proposed method are: 1) it does not require structural approximations of the model, and 2) the aforementioned marginalization is done both in the forward direction and in the backward direction.

Original languageEnglish
Pages (from-to)353-365
Number of pages13
JournalIEEE Journal of Selected Topics in Signal Processing
Volume10
Issue number2
DOIs
Publication statusPublished - Mar 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Monte Carlo methods
  • particle filters
  • particle smoothers
  • Rao-Blackwellization
  • backward sampling
  • MONTE-CARLO METHODS
  • STATE-SPACE MODELS
  • BAYESIAN-INFERENCE
  • TARGET TRACKING
  • FILTERS

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