New criteria for blind source separation using second-order cyclic statistics

Abstract

This paper addresses the problem of blind separation of cyclostationary sources. By using the cyclostationarity property of the source signals, new criteria based on second-order cyclic statistics (SOCS) are established, from which two algorithms for blind source separation are proposed. Compared with the existing higher-order statistics-based approaches, our new approach requires few data samples and does not impose any restrictions on the probability distributions of the source signals. Simulation results are given to demonstrate the effectiveness of this new approach.