Rao-Blackwellized Gaussian Smoothing

Roland Hostettler; Simo Särkkä

doi:10.1109/TAC.2018.2828087

Rao-Blackwellized Gaussian Smoothing

Roland Hostettler, Simo Särkkä

Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

14 Sitaatiot (Scopus)

333 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Artikkeli	8340820
Sivut	305-312
Sivumäärä	8
Julkaisu	IEEE Transactions on Automatic Control
Vuosikerta	64
Numero	1
Varhainen verkossa julkaisun päivämäärä	18 huhtik. 2018
DOI - pysyväislinkit	https://doi.org/10.1109/TAC.2018.2828087
Tila	Julkaistu - 1 tammik. 2019
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Pääsy asiakirjaan

10.1109/TAC.2018.2828087

ELEC_Hostettler_Sarkka_Rao-Blackwellized_IEEETraAutCon_64_authoracceptedmanuscriptAccepted author manuscript, 326 KB

Muut tiedostot ja linkit

Link to publication in Scopus

Ympäristön joukkoistettu kartoittaminen - monilähteinen ajantasainen SLAM käyttäen liike-, valo- ja magneettiantureita
Hostettler, R., Särkkä, S., Tronarp, F., Garcia Fernandez, A., Sarmavuori, J., Karvonen, T. & Raitoharju, M.
01/01/2016 → 31/12/2017
Projekti: Academy of Finland: Other research funding
Sekventiaalisia Monte Carlo -menetelmiä tila- ja parametriestimointiin stokastisissa dynaamisissa systeemeissä
Särkkä, S.
01/06/2015 → 31/08/2018
Projekti: Academy of Finland: Other research funding

Siteeraa tätä

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abstract = "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.",

keywords = "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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year = "2019",

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doi = "10.1109/TAC.2018.2828087",

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AB - 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.

KW - Computational modeling

KW - Density measurement

KW - Gaussian assumed density smoothing

KW - Kalman filters

KW - Linear regression

KW - Noise measurement

KW - Nonlinear smoothing

KW - Nonlinear state estimation

KW - Rao-Blackwellization

KW - Smoothing methods

KW - State-space methods

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JO - IEEE Transactions on Automatic Control

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IS - 1

M1 - 8340820

ER -

Rao-Blackwellized Gaussian Smoothing

Abstrakti

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

Ympäristön joukkoistettu kartoittaminen - monilähteinen ajantasainen SLAM käyttäen liike-, valo- ja magneettiantureita

Sekventiaalisia Monte Carlo -menetelmiä tila- ja parametriestimointiin stokastisissa dynaamisissa systeemeissä

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