CUED Publications database

Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models

Lindsten, F and Bunch, P and Särkkä, S and Schön, TB and Godsill, SJ (2016) Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models. IEEE Journal on Selected Topics in Signal Processing, 10. pp. 353-365. ISSN 1932-4553

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© 2015 IEEE. Sequential Monte Carlo (SMC) methods, such as the particle filter, are by now one of the standard computational techniques for addressing the filtering problem in general state-space models. However, many applications require post-processing of data offline. In such scenarios the smoothing problem - in which all the available data is used to compute state estimates - is of central interest. We consider the smoothing problem for a class of conditionally linear Gaussian models. We present a forward-backward-type Rao-Blackwellized particle smoother (RBPS) that is able to exploit the tractable substructure present in these models. Akin to the well known Rao-Blackwellized particle filter, the proposed RBPS marginalizes out a conditionally tractable subset of state variables, effectively making use of SMC only for the 'intractable part' of the model. Compared to existing RBPS, two key features of the proposed method are: 1) it does not require structural approximations of the model, and 2) the aforementioned marginalization is done both in the forward direction and in the backward direction.

Item Type: Article
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:11
Last Modified: 22 Oct 2019 08:24
DOI: 10.1109/JSTSP.2015.2506543