Iterative Filtering and Smoothing In Non-Linear and Non-Gaussian Systems Using Conditional Moments

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Authors

Date

2018-01-17

Department

Department of Electrical Engineering and Automation

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Mcode

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Language

en

Pages

5

Series

IEEE Signal Processing Letters, Volume 25, issue 3, pp. 408-412

Abstract

This letter presents the development of novel iterated filters and smoothers that only require specification of the conditional moments of the dynamic and measurement models. This leads to generalisations of the iterated extended Kalman filter, the iterated extended Kalman smoother, the iterated posterior linearisation filter, and the iterated posterior linearisation smoother. The connections to the previous algorithms are clarified and a convergence analysis is provided. Furthermore, the merits of the proposed algorithms are demonstrated in simulations of the stochastic Ricker map where they are shown to have similar or superior performance to competing algorithms.

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Keywords

iterative methods, statistical linear regression, non-linear/non-Gaussian systems, State estimation

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DOI

10.1109/LSP.2018.2794767

Citation

Tronarp, F, Garcia Fernandez, A & Särkkä, S 2018, 'Iterative Filtering and Smoothing In Non-Linear and Non-Gaussian Systems Using Conditional Moments', IEEE Signal Processing Letters, vol. 25, no. 3, pp. 408-412. https://doi.org/10.1109/LSP.2018.2794767

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https://urn.fi/URN:NBN:fi:aalto-201803231782

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[article-cris] Sähkötekniikan korkeakoulu / ELEC