Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/115202
Title: New criteria for blind source separation using second-order cyclic statistics
Authors: Liang, Y.-C. 
Rahim Leyman, A.
Chin, F. 
Keywords: Adaptive algorithm
Blind source separation
Convergence analysis
Second-order cyclic statistics
Issue Date: 2000
Citation: Liang, Y.-C.,Rahim Leyman, A.,Chin, F. (2000). New criteria for blind source separation using second-order cyclic statistics. Circuits, Systems, and Signal Processing 19 (1) : 43-58. ScholarBank@NUS Repository.
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.
Source Title: Circuits, Systems, and Signal Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/115202
ISSN: 0278081X
Appears in Collections:Staff Publications

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