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A Signal-Dependent Quadratic Time Frequency Distribution for Neural Source Estimation

  • Pu Wang
  • Jianyu Yang
  • Zhi-Lin Zhang
  • Gang Wang
  • Quanyi Mo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

A novel method for kernel design of a quadratic time frequency distribution (TFD) as the initial step for neural source estimation is proposed. The kernel is constructed based on the product ambiguity function (AF), which efficiently suppresses cross terms and noise in the ambiguity domain. In order to reduce the influence from the strong signal to the weak signal, an iterative approach is implemented. Simulation results validate the method and demonstrate suppression of cross terms and noise, and high resolution in the time frequency domain.

Keywords

Cross Term Ambiguity Function Chirp Signal Time Frequency Domain Chirp Rate 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pu Wang
    • 1
    • 2
  • Jianyu Yang
    • 1
  • Zhi-Lin Zhang
    • 2
  • Gang Wang
    • 2
  • Quanyi Mo
    • 2
  1. 1.School of Electronic EngineeringUniversity of Electronic Science and Technology of ChinaChengduP.R. China
  2. 2.Blind Source Separation Research GroupUniversity of Electronic Science and Technology of ChinaChengduP.R. China

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