Title: Efficient blind nonparametric dependent signal extraction algorithm for determined and underdetermined mixtures

Authors: Fasong Wang; Rui Li

Addresses: School of Information Engineering, Zhengzhou University, Zhengzhou, 450001, China ' School of Sciences, Henan University of Technology, Zhengzhou, 450001, China

Abstract: Blind extraction or separation statistically independent source signals from their linear mixtures have been well studied in the last two decades, which is realised by searching for local extrema of certain objective functions. In this paper, by employing nonparametric non-Gaussianity (NG) measure, a blind source separation/extraction (BSS/BSE) algorithm is derived to separate or extract statistically dependent source signals from their underdetermined or determined linear mixtures. Firstly, we show that maximisation NG measure can separate/extract statistically weak dependent source signals. Then, the nonparametric NG measure is defined by statistical distances between different cumulative distribution function (CDF) of separated signals, which can be estimated by quantiles and order statistics (OS) using L2 norm efficiently. Finally, the nonparametric NG measure aided algorithm is optimised by a deflation procedure. Simulation results for synthesis and real world data show that the proposed algorithm can extract the desired dependent source signals and yield expected performance.

Keywords: blind source separation; non-gaussianity measure; independent component analysis; PDF; probability density function; dependent component analysis; underdetermined blind source extraction.

DOI: 10.1504/IJISTA.2019.101944

International Journal of Intelligent Systems Technologies and Applications, 2019 Vol.18 No.5, pp.509 - 530

Received: 03 Feb 2017
Accepted: 07 Aug 2017

Published online: 13 Aug 2019 *

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