Rao-Blackwellized Particle Smoothers for Mixed Linear/Nonlinear State-Space Models
2013 (English)In: Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing, IEEE conference proceedings, 2013, 6288-6292 p.Conference paper (Refereed)
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
IEEE conference proceedings, 2013. 6288-6292 p.
Rao-Blackwellization, Particle smoothing, Backward simulation, Sequential Monte Carlo
Signal Processing Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-93460DOI: 10.1109/ICASSP.2013.6638875ISI: 000329611506090OAI: oai:DiVA.org:liu-93460DiVA: diva2:624911
38th International Conference on Acoustics, Speech, and Signal Processing, Vancouver, Canada, 26-31 May, 2013
FunderSwedish Research Council, 621-2010-5876