Importance Densities for Particle Filtering Using Iterated Conditional Expectations

Roland Hostettler, Filip Tronarp, Ángel F. García-Fernández, Simo Särkkä

Abstract: In this letter, we consider Gaussian approximations of the optimal importance density in sequential importance sampling for nonlinear, non-Gaussian state-space models. The proposed method is based on generalized statistical linear regression and posterior linearization using conditional expectations. Simulation results show that the method outperforms the compared methods in terms of the effective sample size and provides a better local approximation of the optimal importance density.