Importance Densities for Particle Filtering Using Iterated Conditional Expectations

Hostettler, Roland, Tronarp, Filip, Garcia-Fernandez, Angel F ORCID: 0000-0002-6471-8455 and Sarkka, Simo (2020) Importance Densities for Particle Filtering Using Iterated Conditional Expectations. IEEE SIGNAL PROCESSING LETTERS, 27. pp. 211-215.

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Official URL: http://dx.doi.org/10.1109/lsp.2020.2964531

Abstract

In this letter, we consider Gaussian approximations of the optimal importance density in sequential importance sampling for nonlinear, non-Gaussian state-space models. The proposed method is based on generalized statistical linear regression and posterior linearization using conditional expectations. Simulation results show that the method outperforms the compared methods in terms of the effective sample size and provides a better local approximation of the optimal importance density.

Item Type:	Article
Uncontrolled Keywords:	State estimation, particle filters, Monte Carlo methods, nonlinear systems, posterior linearization
Depositing User:	Symplectic Admin
Date Deposited:	09 Jan 2020 08:51
Last Modified:	16 Mar 2024 02:20
DOI:	10.1109/LSP.2020.2964531
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3069880