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

• Main Authors: Lindsten, Fredrik, Bunch, Pete, Godsill, Simon J., Schön, Thomas B.
• Format: Others
• Language: English
• Published: Linköpings universitet, Reglerteknik 2013
• Subjects: Rao-Blackwellization; particle smoothing; backward simulation; sequential Monte Carlo
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93460

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