Iterated Posterior Linearization Smoother



Garcia-Fernandez, Angel F ORCID: 0000-0002-6471-8455, Svensson, Lennart and Sarkka, Simo
(2017) Iterated Posterior Linearization Smoother. IEEE Transactions on Automatic Control, 62 (4). pp. 2056-2063.

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Abstract

This note considers the problem of Bayesian smoothing in nonlinear state-space models with additive noise using Gaussian approximations. Sigma-point approximations to the general Gaussian Rauch-Tung-Striebel smoother are widely used methods to tackle this problem. These algorithms perform statistical linear regression (SLR) of the nonlinear functions considering only the previous measurements. We argue that SLR should be done taking all measurements into account. We propose the iterated posterior linearization smoother (IPLS), which is an iterated algorithm that performs SLR of the nonlinear functions with respect to the current posterior approximation. The algorithm is demonstrated to outperform conventional Gaussian nonlinear smoothers in two numerical examples.

Item Type: Article
Uncontrolled Keywords: Bayesian smoothing, iterated smoothing, Rauch-Tung-Striebel smoothing, sigma-points, statistical linear regression
Depositing User: Symplectic Admin
Date Deposited: 03 Jan 2018 15:17
Last Modified: 19 Jan 2023 06:46
DOI: 10.1109/TAC.2016.2592681
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3015339