Iterated Posterior Linearization Smoother
Artikel i vetenskaplig tidskrift, 2017

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.

statistical

iterated smoothing

Rauch-Tung-Striebel smoothing

Bayesian smoothing

Gauss-Newton Method

sigma-points

Författare

Angel Garcia

Curtin University

Lennart Svensson

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

S. Särkkä

Aalto-Yliopisto

IEEE Transactions on Automatic Control

0018-9286 (ISSN)

Vol. 62 2056-2063

Ämneskategorier

Elektroteknik och elektronik

DOI

10.1109/TAC.2016.2592681