Journal of Northeastern University Natural Science ›› 2015, Vol. 36 ›› Issue (5): 614-617.DOI: 10.12068/j.issn.1005-3026.2015.05.002

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A New Complex Blind Source Separation Algorithm Based on Standard Kurtosis

JI Ce, WANG Yan-ru, WANG Xiao-yu   

  1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China.
  • Received:2014-04-04 Revised:2014-04-04 Online:2015-05-15 Published:2014-11-07
  • Contact: WANG Xiao-yu
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Abstract: In the complex blind source separation algorithm, the complex signal kurtosis maximization is often used as the cost function. The complex standard kurtosis was used instead of complex kurtosis as the new cost function for optimimization, and a modified complex quasi-newton iterative algorithm was employed to optimize the cost function. The algorithm was applied to separate mixed QAM signal, and simulation results showed that the improved algorithm has a good separation effect. Compared with the algorithm of the kurtosis maximization as the cost function, the convergence performance was improved obviously.

Key words: complex valued, kurtosis, standard kurtosis, cost function, independent component analysis(ICA)

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