Iterative Filtering and Smoothing In Non-Linear and Non-Gaussian Systems Using Conditional Moments

Filip Tronarp, Angel Garcia Fernandez, Simo Särkkä

Research output: Contribution to journalArticleScientificpeer-review

17 Citations (Scopus)
281 Downloads (Pure)

Abstract

This letter presents the development of novel iterated filters and smoothers that only require specification of the conditional moments of the dynamic and measurement models. This leads to generalisations of the iterated extended Kalman filter, the iterated extended Kalman smoother, the iterated posterior linearisation filter, and the iterated posterior linearisation smoother. The connections to the previous algorithms are clarified and a convergence analysis is provided. Furthermore, the merits of the proposed algorithms are demonstrated in simulations of the stochastic Ricker map where they are shown to have similar or superior performance to competing algorithms.
Original languageEnglish
Pages (from-to)408-412
Number of pages5
JournalIEEE Signal Processing Letters
Volume25
Issue number3
DOIs
Publication statusPublished - 17 Jan 2018
MoE publication typeA1 Journal article-refereed

Keywords

  • iterative methods
  • statistical linear regression
  • non-linear/non-Gaussian systems
  • State estimation

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