CUED Publications database

A backward-simulation based Rao-Blackwellized particle smoother for conditionally linear Gaussian models

Särkkä, S and Bunch, P and Godsill, SJ (2012) A backward-simulation based Rao-Blackwellized particle smoother for conditionally linear Gaussian models. IFAC Proceedings Volumes (IFAC-PapersOnline), 16. pp. 506-511. ISSN 1474-6670

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Abstract

In this article, we develop a new Rao-Blackwellized Monte Carlo smoothing algorithm for conditionally linear Gaussian models. The algorithm is based on the forward-filtering backward-simulation Monte Carlo smoother concept and performs the backward simulation directly in the marginal space of the non-Gaussian state component while treating the linear part analytically. Unlike the previously proposed backward-simulation based Rao-Blackwellized smoothing approaches, it does not require sampling of the Gaussian state component and is also able to overcome certain normalization problems of two-filter smoother based approaches. The performance of the algorithm is illustrated in a simulated application. © 2012 IFAC.

Item Type: Article
Uncontrolled Keywords: Estimation algorithms Monte Carlo method Nonlinear systems Optimal estimation Stochastic systems
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron job
Date Deposited: 04 Feb 2015 23:02
Last Modified: 05 Feb 2015 06:46
DOI: 10.3182/20120711-3-BE-2027.00115