A blind source separation technique using second-order statistics

• Published in: IEEE transactions on signal processing Vol. 45; no. 2; pp. 434 - 444
• Main Authors: Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F., Moulines, E.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-02-1997; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Array signal processing; Blind source separation; Calibration; Covariance matrix; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Frequency estimation; Information, signal and communications theory; Narrowband; Sensor arrays; Signal and communications theory; Signal processing; Signal, noise; Statistics; Telecommunications and information theory; Vectors; Matrix diagonalization; Performance evaluation; Statistical method; Asymptotic behavior; Signal estimation; Signal processing; Identification; Algorithm
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/78.554307

Separation of sources consists of recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori information on the mixing matrix is available: The linear mixture should be "blindly" processed. This typically occurs in narrowband array processing applications when the array manifold is unknown or distorted. This paper introduces a new source separation technique exploiting the time coherence of the source signals. In contrast with other previously reported techniques, the proposed approach relies only on stationary second-order statistics that are based on a joint diagonalization of a set of covariance matrices. Asymptotic performance analysis of this method is carried out; some numerical simulations are provided to illustrate the effectiveness of the proposed method.