Rao-Blackwellized Particle Smoothers for Mixed Linear/Nonlinear State-Space Models
2011 (English)Report (Other academic)
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.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. , 16 p.
LiTH-ISY-R, ISSN 1400-3902 ; 3018
Rao-Blackwellization, Particle smoothing, Backward simulation, Sequential Monte Carlo
IdentifiersURN: urn:nbn:se:liu:diva-97958ISRN: LiTH-ISY-R-3018OAI: oai:DiVA.org:liu-97958DiVA: diva2:650797
FunderSwedish Research Council, 621-2010-5876