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Adaptive Robust Super-exponential Algorithms for Deflationary Blind Equalization of Instantaneous Mixtures

  • Masanori Ito
  • Masashi Ohata
  • Mitsuru Kawamoto
  • Toshiharu Mukai
  • Yujiro Inouye
  • Noboru Ohnishi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3195)

Abstract

The so called “super-exponential” algorithms (SEA’s) are attractive algorithms for solving blind signal processing problems. The conventional SEA’s, however, have such a drawback that they are very sensitive to Gaussian noise. To overcome this drawback, we propose a new SEA. While the conventional SEA’s use the second- and higher-order cumulants of observations, the proposed SEA uses only the higher-order cumulants of observations. Since higher-order cumulants are insensitive to Gaussian noise, the proposed SEA is robust to Gaussian noise, which is referred to as a robust super-exponential algorithm (RSEA). The proposed RSEA is implemented as an adaptive algorithm, which is referred to as an adaptive robust super-exponential algorithm (ARSEA). To show the validity of the ARSEA, some simulation results are presented.

Keywords

Gaussian Noise Independent Component Analysis Independent Component Analysis Blind Deconvolution Blind Equalization 
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 2004

Authors and Affiliations

  • Masanori Ito
    • 1
  • Masashi Ohata
    • 2
  • Mitsuru Kawamoto
    • 2
    • 3
  • Toshiharu Mukai
    • 2
  • Yujiro Inouye
    • 3
  • Noboru Ohnishi
    • 1
  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan
  2. 2.Bio-mimetic Control Research CenterRIKENNagoyaJapan
  3. 3.Department of Electronic and Control Systems EngineeringShimane UniversityShimaneJapan

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