Maximum Likelihood Source Separation By the Expectation-Maximization Technique: Deterministic and Stochastic Implementation.

This paper deals with the source separation problem which consists in the separation of a mixture of independent sources without a priori knowledge on the mixing matrix. When the source distributions are known in advance, this problem can be solved via the Maximum Likelihood (ML) approach by maximizing the data likelihood function using (i) the ExpectationMaximization (EM) algorithm and (ii) a stochastic version of it, the SEM. Two important features of our algorithm are that (a) the covariance of the additive noise can be estimated as a regular parameter, (b) in the case of discrete sources, it is possible to separate more sources than sensors. The effectiveness of this method is illustrated by numerical simulations. I. Introduction When an array of m sensors samples the fields radiated by n narrow band sources its output is classically modeled as an instantaneous spatial mixture of a random vector made of m one-dimensional components, possibly corrupted by additive noise. The sourc..

Underdetermined Blind Separation Of Fm-Like Signals

this article, we focus on the challenging problem of underdetermined blind source separation (UBSS) where the number of sources is larger than the number of sensors. The sources under consideration are non--stationary frequency--modulated (FM) signals encountered in radar and certain wireless communication systems. For instance, linear FM chirp signaling is considered for frequency--hopped CDMA (Code Division Multiple Access) systems [1--3] in which context UBSS coincides with the blind multiuser detection proble