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 a n d 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.