Importance Densities for Particle Filtering Using Iterated Conditional Expectations

Research output: Contribution to journalArticleScientificpeer-review


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

Research units

  • Uppsala University
  • University of Liverpool
  • Antonio de Nebrija University


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

    Research areas

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

ID: 41155770