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Iterated Posterior Linearization Smoother

Angel Garcia-Fernandez ; Lennart Svensson (Institutionen för signaler och system, Signalbehandling) ; S. Sarkka
Ieee Transactions on Automatic Control (0018-9286). Vol. 62 (2017), 4, p. 2056-2063.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Bayesian smoothing, iterated smoothing, Rauch-Tung-Striebel smoothing, sigma-points, statistical, Gauss-Newton Method

Denna post skapades 2017-05-15.
CPL Pubid: 249335


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Institutionen för signaler och system, Signalbehandling (1900-2017)


Elektroteknik och elektronik

Chalmers infrastruktur