Evolution strategies based Gaussian sum particle filter for nonlinear state estimation

There has been significant recent interest of particle filters for nonlinear state estimation. Particle filters evaluate the grid sum approximation of a posterior probability distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. We propose in this paper a particle filter, which combines the ideas of Gaussian sum filter based on the Gaussian mixture approximation of the posteriori distribution and evolution strategies based particle filter using selection process in evolution strategies. Numerical simulation study indicates the potential to create high performance filters for nonlinear state estimation.