Rao-Blackwellized Gaussian Smoothing

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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.

Details

Original languageEnglish
Article number8340820
Pages (from-to)305-312
Number of pages8
JournalIEEE Transactions on Automatic Control
Volume64
Issue number1
Early online date18 Apr 2018
Publication statusPublished - 1 Jan 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • 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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