Optimisation de fonction de contraste en séparation de sources
Benoît Stoll · HAL (Le Centre pour la Communication Scientifique Directe) · 2000
Blind Source Separation aim to recover a set of M independent signals called sources from the observation of N mixtures. Several Source Separation methods exist, most of them are based on Higher Order Statistics. Those methods exploit the source independence hypothesis. Amongthem we consider the case of the source separation based on contrast function optimization in a spatial linear mixture case.We rst propose two contrast families including as a particular case some existing contrasts. Then we determine the optimal solution in a two sources case for this couple of contrast families, thus proposing two algorithms which constitute two classic algorithm generalizations. Then, we study constrained contrast optimization in order to propose algorithms which don't need, as before, data pre-whitening. Two direct constrained optimization method families are considered : the dual methods and the direct methods. Thus we can develop algorithms using Lagrangian concept, penalization concept and a concept of projecting onto the constraint. Computer simulations illustrate the behaviour of the algorithms