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Importance Densities for Particle Filtering Using Iterated Conditional Expectations
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.
Aalto Univ, Dept Elect Engn & Automat, Espoo 02150, Finland.
Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3BX, Merseyside, England;Univ Antonio de Nebrija, ARIES Res Ctr, Madrid 28015, Spain.
Aalto Univ, Dept Elect Engn & Automat, Espoo 02150, Finland.
2020 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 27, p. 211-215Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2020. Vol. 27, p. 211-215
Keywords [en]
State estimation, particle filters, Monte Carlo methods, nonlinear systems, posterior linearization
National Category
Probability Theory and Statistics Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-406508DOI: 10.1109/LSP.2020.2964531ISI: 000511411900013OAI: oai:DiVA.org:uu-406508DiVA, id: diva2:1413472
Funder
Academy of FinlandAvailable from: 2020-03-10 Created: 2020-03-10 Last updated: 2020-03-23Bibliographically approved

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Hostettler, Roland

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