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Blind Nonparametric Determined and Underdetermined Signal Extraction Algorithm for Dependent Source Mixtures

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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Abstract

Blind extraction or separation statistically independent source signals from linear mixtures have been well studied in the last two decades by searching for local extrema of certain objective functions, such as nonGaussianity (NG) measure. Blind source extraction (BSE) algorithm from underdetermined linear mixtures of the statistically dependent source signals is derived using nonparametric NG measure in this paper. After showing that maximization of the NG measure can also separate or extract the statistically weak dependent source signals, the nonparametric NG measure is defined by statistical distances between different source signals distributions based on the cumulative density function (CDF) instead of traditional probability density function (PDF), which can be estimated by the quantiles and order statistics using the \( L^{2} \) norm efficiently. The nonparametric NG measure can be optimized by a deflation procedure to extract or separate the dependent source signals. Simulation results for synthesis and real world data show that the proposed nonparametric extraction algorithm can extract the dependent signals and yield ideal performance.

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Acknowledgments

This research is financially supported by the National Natural Science Foundation of China (No. 61401401, 61172086, 61402421, U1204607), the China Postdoctoral Science Foundation (No. 2014M561998) and the young teachers special Research Foundation Project of Zhengzhou University (No. 1411318029).

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Authors and Affiliations

  1. School of Information Engineering, Zhengzhou University, Zhengzhou, 450001, China

    Fasong Wang, Zhongyong Wang & Xiangchuan Gao

  2. School of Sciences, Henan University of Technology, Zhengzhou, China

    Rui Li

Authors
  1. Fasong Wang
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  2. Rui Li
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  3. Zhongyong Wang
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  4. Xiangchuan Gao
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Corresponding author

Correspondence to Fasong Wang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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Wang, F., Li, R., Wang, Z., Gao, X. (2015). Blind Nonparametric Determined and Underdetermined Signal Extraction Algorithm for Dependent Source Mixtures. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

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