Inference in Mixed Linear/Nonlinear State-Space Models using Sequential Monte Carlo

In this work we apply sequential Monte Carlo methods, i.e., particle filters and smoothers, to estimate the state in a certain class of mixed linear/nonlinear state-space models. Such a model has an inherent conditionally linear Gaussian substructure. By utilizing this structure we are able to address even high-dimensional nonlinear systems using Monte Carlo methods, as long as only a few of the states enter nonlinearly. First, we consider the filtering problem and give a self-constained derivation of the well known Rao-Blackellized particle filter. Therafter we turn to the smoothing problem and derive a Rao-Blackwellized particle smoother capable of handling the fully interconnected model under study