Gradient-free maximum likelihood parameter estimation with particle filters

Abstract

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