Variance Reduction for Particle Filters of Systems With Time Scale Separation

We present a particle filter construction for a system that exhibits time-scale separation. The separation of time scales allows two simplifications that we exploit: 1) the use of the averaging principle for the dimensional reduction of the dynamics for each particle during the prediction step and 2) the factorization of the transition probability for the Rao-Blackwellization of the update step. The resulting particle filter is faster and has smaller variance than the particle filter based on the original system. The method is tested on a multiscale stochastic differential equation and on a multiscale pure jump diffusion motivated by chemical reactions.