Evolution Strategies Based Particle Filters for Nonlinear State Estimation

抄録

Estimation of dynamical systems is one of the most important problems in control systems engineering. Particle filters have attracted attentions as a tool to solve this problem, especially for nonlinear dynamical systems. They evaluate a posterior probability distribution of the state variable based on observations in simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. A new filter, Evolution Strategies Based Particle Filter, is proposed to circumvent this difficulty and to improve the performance. Numerical simulation results illustrate the applicability of the proposed idea.