Rao-Blackwellized Gaussian Smoothing

Roland Hostettler, Simo Särkkä

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
276 Downloads (Pure)


In this paper, we consider Rao-Blackwellization of linear substructures in sigma-point-based Gaussian assumed density smoothers. We derive marginalized prediction, smoothing, and update steps for the mixed linear/nonlinear Gaussian state-space model as well as for a hierarchical model for both conventional and iterated posterior linearization Gaussian smoothers. The proposed method is evaluated in a numerical example and it is shown that the computational complexity is reduced considerably compared to non-Rao--Blackwellized Gaussian smoothers for systems with high-dimensional linear subspaces.

Original languageEnglish
Article number8340820
Pages (from-to)305-312
Number of pages8
JournalIEEE Transactions on Automatic Control
Issue number1
Early online date18 Apr 2018
Publication statusPublished - 1 Jan 2019
MoE publication typeA1 Journal article-refereed


  • Computational modeling
  • Density measurement
  • Gaussian assumed density smoothing
  • Kalman filters
  • Linear regression
  • Noise measurement
  • Nonlinear smoothing
  • Nonlinear state estimation
  • Rao-Blackwellization
  • Smoothing methods
  • State-space methods


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  • CrowdSLAM

    Hostettler, R., Särkkä, S., Tronarp, F., Garcia Fernandez, A., Sarmavuori, J., Karvonen, T. & Raitoharju, M.


    Project: Academy of Finland: Other research funding

  • Särkkä Simo EEA

    Särkkä, S.


    Project: Academy of Finland: Other research funding

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