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Importance Densities for Particle Filtering Using Iterated Conditional Expectations

Tronarp, Filip LU ; Hostettler, Roland ; Särkkä, Simo and Garcia-Fernandez, Angel F (2020) In IEEE Signal Processing Letters 27. p.211-215
Abstract
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.
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author
; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
state estimation, particle filters, Monte Carlo methods, nonlinear systems, posterior linearization
in
IEEE Signal Processing Letters
volume
27
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85079094614
ISSN
1070-9908
DOI
10.1109/LSP.2020.2964531
language
English
LU publication?
no
id
940c6217-9d62-4c53-aca6-962f63c0ab4d
date added to LUP
2023-08-20 22:59:03
date last changed
2023-10-13 15:39:51
@article{940c6217-9d62-4c53-aca6-962f63c0ab4d,
  abstract     = {{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.}},
  author       = {{Tronarp, Filip and Hostettler, Roland and Särkkä, Simo and Garcia-Fernandez, Angel F}},
  issn         = {{1070-9908}},
  keywords     = {{state estimation; particle filters; Monte Carlo methods; nonlinear systems; posterior linearization}},
  language     = {{eng}},
  pages        = {{211--215}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Signal Processing Letters}},
  title        = {{Importance Densities for Particle Filtering Using Iterated Conditional Expectations}},
  url          = {{http://dx.doi.org/10.1109/LSP.2020.2964531}},
  doi          = {{10.1109/LSP.2020.2964531}},
  volume       = {{27}},
  year         = {{2020}},
}