Abstract
Recent significant increase of the computational power leads to rebirth of Monte Carlo integration and its application of Bayesian filtering, or particle filters. Particle filters evaluate a posteriori probability distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, the filter performance is deteriorated by degeneracy phenomena in the importance weights. To circumvent this difficulty and to improve the performance, a novel filter called the Evolution Strategies (ES) based particle filter, has been proposed by recognizing the similarities and the difference of the processes between the particle filters and ES. In this paper, the proposed filter is applied to simultaneous state and parameter estimation of nonlinear state space models. Results of numerical simulation studies illustrate the applicability of this approach.
