Gradient-free maximum likelihood parameter estimation with particle filters

In this paper we address the problem of on-line estimation of unknown static parameters in non-linear non-Gaussian state-space models. We consider a particle filtering method and employ two gradient-free Stochastic approximation (SA) methods to maximize recursively the likelihood function, the finite difference SA and Spall´s simultaneous perturbation SA. We demonstrate how these algorithms can generate maximum likelihood estimates in a simple and computationally efficient manner. The performance of the proposed algorithms is assessed through simulation