Rao-Blackwellized Auxiliary Particle Filters for Mixed Linear/Nonlinear Gaussian models

The Auxiliary Particle Filter is a variant of the common particle filter which attempts to incorporate information from the next measurement to improve the proposal distribution in the update step. This paper studies how this can be done for Mixed Linear/Nonlinear Gaussian models, it builds on a previously suggested method and introduces two new variants which tries to improve the performance by using a more detailed approximation of the true probability density function when evaluating the so called first stage weights. These algorithms are compared for a couple of models to illustrate their strengths and weaknesses.