Rao-Blackwellized Gaussian Smoothing
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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Author
Date
2019-01-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
8
302 - 309
302 - 309
Series
IEEE Transactions on Automatic Control, Volume 64, issue 1
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.Description
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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Citation
Hostettler , R & Särkkä , S 2019 , ' Rao-Blackwellized Gaussian Smoothing ' , IEEE Transactions on Automatic Control , vol. 64 , no. 1 , 8340820 , pp. 305-312 . https://doi.org/10.1109/TAC.2018.2828087
