Blind Deconvolution Based on Constrained Marginalized Particle Filters

Abstract

This thesis presents a new approach to blind deconvolution algorithms. The proposed method is a combination of a classical blind deconvolution subspace method and a marginalized particle filter. It is shown that the new method provides better performance than just a marginalized particle filter, and better robustness than the classical subspace method. The properties of the new method make it a candidate for further exploration of its potential application in acoustic blind dereverberation.