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IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2006 E89-A(8):2149-2157; doi:10.1093/ietfec/e89-a.8.2149
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Copyright © 2006 The Institute of Electronics, Information and Communication Engineers

Special Section on Papers Selected from the 20th Symposium on Signal Processing -- Papers

A Characteristic Function Based Contrast Function for Blind Extraction of Statistically Independent Signals*

Muhammad TUFAIL1, Masahide ABE1 and Masayuki KAWAMATA1

1 The authors are with the Graduate School of Engineering, Tohoku University, Sendai-shi, 980-8579 Japan. E-mail: tufail{at}mk.ecei.tohoku.ac.jp

In this paper, we propose to employ a characteristic function based non-Gaussianity measure as a one-unit contrast function for independent component analysis. This non-Gaussianity measure is a weighted distance between the characteristic function of a random variable and a Gaussian characteristic function at some adequately chosen sample points. Independent component analysis of an observed random vector is performed by optimizing the above mentioned contrast function (for different units) using a fixed-point algorithm. Moreover, in order to obtain a better separation performance, we employ a mechanism to choose appropriate sample points from an initially selected sample vector. Finally, some computer simulations are presented to demonstrate the validity and effectiveness of the proposed method.

Key Words: independent component analysis, characteristic function, non-Gaussianity measure, contrast function

Manuscript received December 20, 2005. Manuscript revised March 20, 2006. Final manuscript received April 21, 2006.

* This work was supported by the Monbukagakusho Government of Japan.


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