Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments

Tronarp, Filip, Garcia-Fernandez, Angel F ORCID: 0000-0002-6471-8455 and Sarkka, Simo (2018) Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments IEEE SIGNAL PROCESSING LETTERS, 25 (3). pp. 408-412. ISSN 1070-9908, 1558-2361

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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 generalizations of the iterated extended Kalman filter, the iterated extended Kalman smoother, the iterated posterior linearization filter, and the iterated posterior linearization 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 a similar or superior performance to competing algorithms.

Item Type:	Article
Uncontrolled Keywords:	Iterative methods, nonlinear/non-Gaussian systems, state estimation, statistical linear regression (SLR)
Depositing User:	Symplectic Admin
Date Deposited:	22 Jan 2018 09:17
Last Modified:	16 Jun 2026 03:55
DOI:	10.1109/LSP.2018.2794767
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3016602
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