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.
ISSN 1070-9908, 1558-2361
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PL_PF_accepted_full.pdf - Author Accepted Manuscript Download (569kB) | Preview |
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 |
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| 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: | 22 May 2026 15:24 |
| DOI: | 10.1109/LSP.2020.2964531 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3069880 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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