Blind Complex Source Separation Based on Cyclostationary Statistics

Abstract:

This paper analyzed a blind source separation algorithm based on cyclic frequency of complex signals. Under the blind source separation model, we firstly gave several useful assumptions. Then we discussed the derivation of the BSS algorithm, including the complex signals and the normalization situation. Later, we analyzed the complex WCW-CS algorithm, which was compared with NGA, NEASI and NGA-CS algorithms. Simulation results show that the complex WCW-CS algorithm has the best convergence and separation performance. It can also effectively separate mixed image signals, whose performance was better than NGA algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 519-520)
• Pages: 1051-1056
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.519-520.1051
• Online since: February 2014
• Authors: Jie Guo*, An Quan Wei, Lei Tang
• Keywords: Blind Source Separation, Complex Signals, Cyclic Frequency, Cyclostationary

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