Rao-Blackwellized particle smoothers for conditionally linear Gaussian models

TL;DR

This paper introduces a Rao-Blackwellized particle smoother for conditionally linear Gaussian models, enabling efficient offline smoothing by exploiting model structure without structural approximations.

Contribution

It presents a novel forward-backward Rao-Blackwellized particle smoother that leverages model structure for improved smoothing without structural approximations.

Findings

Effective exploitation of model structure in smoothing

No need for structural model approximations

Bidirectional marginalization enhances performance

Abstract

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: (i) it does not require structural approximations of the model, and (ii) the aforementioned marginalization is done both in the forward direction and in the backward direction.