Blind identification of underdetermined mixtures based on the hexacovariance and higher-order cyclostationarity

In this work, we consider the problem of blind identification of underdetermined mixtures in a cyclostationary context relying on sixthorder statistics. We propose to exploit the cyclostationarity at higher orders by taking into account the knowledge of source cyclic frequencies in the sample estimator of the observation hexacovariance. Two blind identification algorithms based on the proposed estimator are considered and their performances are tested by means of computer simulations. Our simulation results show that significant improvements can be obtained when both second and fourth-order cyclo-stationarities are exploited.