Blind Source Separation Based on Generalized Variance

  • Gaoming Huang
  • Luxi Yang
  • Zhenya He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


In this paper, a novel blind source separation (BSS) algorithm based on generalized variance is proposed according to the property of multivariable statistical analysis. This separation contrast function of this algorithm is based on second order moments. It can complete the blind separation of supergaussian and subgaussian signals at the same time without adjusting the learning function The restriction of this algorithm is not too much and the computation burden is light. Simulation results confirm that the algorithm is statistically efficient for all practical purpose and the separation effect is very feasible.


Independent Component Analysis Blind Source Separation Blind Signal Computation Burden Gaussian Source 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jutten, C., Herault, J.: Blind Separation of Sources, Part I: An Adaptive Algorithm Based on Neuromimetic. Signal Processing 24, 1–10 (1991)MATHCrossRefGoogle Scholar
  2. 2.
    David, V., Sánchez, A.: Frontiers of Research in BSS/ICA. Neurocomputing (49), 7–23 (2002)Google Scholar
  3. 3.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. J. Wiley, Chichester (2001)CrossRefGoogle Scholar
  4. 4.
    Mansour, A., Barros, A.K., Ohnishi, N.: Blind Separtion of Sources: Methods, Assumptions and Application. Special Issue on Digital Signal Processing in IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E83-A (8), 1498–1512 (2000)Google Scholar
  5. 5.
    Cadoso, J.F., Jutten, C., Loubaton, P.: Preface of the ICA. In: Proceedings. First Int. Workshop on Independent Component Analysis and Signal Separation (ICA 1999), Aussois, France, pp. 11–15 (1999)Google Scholar
  6. 6.
    Huang, G.M., Yang, L.X., He, Z.Y.: Blind Source Separation Using for Time-Delay Direction Finding. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 660–665. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Huang, G.M., Yang, L.X., He, Z.Y.: Application of Blind Source Separation to a Novel Passive Location. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 1134–1141. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Huang, G.M., Yang, L.X., He, Z.Y.: Application of Blind Source Separation to Time Delay Estimation in Interference Environments. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 496–501. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gaoming Huang
    • 1
    • 2
  • Luxi Yang
    • 2
  • Zhenya He
    • 2
  1. 1.Naval University of EngineeringWuhanChina
  2. 2.Department of Radio EngineeringSoutheast UniversityChina

Personalised recommendations