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Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models

We consider the smoothing problem for a class of conditionally linear Gaussian state-space (CLGSS) models, referred to as mixed linear/nonlinear models. In contrast to the better studied hierarchical CLGSS models, these allow for an intricate cross dependence between the linear and the nonlinear parts of the state vector. We derive a Rao-Blackwellized particle smoother (RBPS) for this model class by exploiting its tractable substructure. The smoother is of the forward filtering/backward simulation type. A key feature of the proposed method is that, unlike existing RBPS for this model class, the linear part of the state vector is marginalized out in both the forward direction and in the backward direction. / CNDM / CADICS

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-93460
Date January 2013
CreatorsLindsten, Fredrik, Bunch, Pete, Godsill, Simon J., Schön, Thomas B.
PublisherLinköpings universitet, Reglerteknik, Linköpings universitet, Tekniska högskolan, Linköpings universitet, Reglerteknik, Linköpings universitet, Tekniska högskolan, Department of Engineering, University of Cambridge, Cambridge, UK, Department of Engineering, University of Cambridge, Cambridge, UK
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeConference paper, info:eu-repo/semantics/conferenceObject, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationProceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), p. 6288-6292

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