Please use this identifier to cite or link to this item:
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
ISSN: 0278081X
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.