On Approximate Maximum Likelihood Methods for Blind Identification: How to Cope with the Curse of Dimensionality

We discuss approximate maximum likelihood methods for blind identification and deconvolution. These algorithms are based on particle approximation versions of the EM algorithm. We consider two different methods which differ in the way the posterior distribution of the symbols is computed. The first algorithm is based on a novel particle approximation method of the fixed-interval smoothing whereas the second uses fixed lag smoothing. We compare the two algorithms in a Monte-Carlo experiment; these two methods perform significantly better than the EMVA algorithm, which is considered as the state of the art in this area.