Evolution strategies based particle filters for state and parameter estimation on nonlinear models

The massive increase of computational power has led to the rebirth of Monte Carlo integration and its application of Bayesian filtering, or particle filters. Particle filters evaluate a posterior probability distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, the filter performance is degraded by degeneracy phenomena in the importance weights. A filter called the evolution strategies (ES)-based particle filter has been proposed to circumvent this difficulty and improve the performance by recognizing the similarities and the difference between the particle filters and ES. The SIE filter is applied to simultaneous state and parameter estimation of nonlinear state space models. Numerical simulation studies have been conducted to exemplify the applicability of this approach.