Journal of Northeastern University Natural Science ›› 2018, Vol. 39 ›› Issue (8): 1108-1113.DOI: 10.12068/j.issn.1005-3026.2018.08.009

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Underdetermined Blind Separation Based on Improved EMD Algorithm

JI Ce, SUN Meng-xue, ZHANG Jun   

  1. School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China.
  • Received:2017-04-07 Revised:2017-04-07 Online:2018-08-15 Published:2018-09-12
  • Contact: SUN Meng-xue
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Abstract: In order to improve the effect of data fitting, a method of extremum extension on endpoints is proposed, which is aimed at the endpoint effect of empirical mode decomposition (EMD) algorithm. The improved EMD algorithm is used to decompose the observed signals, and then the decomposed components together with the prior observed signals are regarded as new observed signals. Thus the underdetermined situation is changed into an overdetermined case. Finally, we use independent component analysis (ICA) algorithm to obtain the estimation of source signals. Simulation result shows that the proposed algorithm is effective.

Key words: underdetermined blind source separation, EMD(empirical mode decomposition), endpoint effect, extremum extension, ICA(independent component analysis)

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