Nonlinear state estimation by evolution strategies based particle filters

There has been significant recent interest of particle filters for nonlinear state estimation. Particle filters evaluate 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. By recognizing the similarities and the difference of the processes between the particle filters and evolution strategies, a new filter, evolution strategies based particle filter, is proposed to circumvent this difficulty and to improve the performance. The applicability of the proposed idea is illustrated by numerical studies.