Importance Densities for Particle Filtering Using Iterated Conditional Expectations

Roland Hostettler*, Filip Tronarp, Angel F. Garcia-Fernandez, Simo Sarkka

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)


In this letter, we consider Gaussian approximations of the optimal importance density in sequential importance sampling for nonlinear, non-Gaussian state-space models. The proposed method is based on generalized statistical linear regression and posterior linearization using conditional expectations. Simulation results show that the method outperforms the compared methods in terms of the effective sample size and provides a better local approximation of the optimal importance density.

Original languageEnglish
Article number8951063
Pages (from-to)211-215
Number of pages5
JournalIEEE Signal Processing Letters
Publication statusPublished - 1 Jan 2020
MoE publication typeA1 Journal article-refereed


  • Monte Carlo methods
  • Nonlinear systems
  • Particle filters
  • Posterior linearization
  • State estimation


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