Abstract
The purpose of this paper is to develop novel Blind Source Separation (BSS) algorithms from linear mixtures of them, which enable to separate dependent source signals. Most of the proposed algorithms for solving BSS problem rely on independence or at least uncorrelation assumption of the source signals. Here, we show that maximization of the nonGaussianity(NG) measure can separate the statistically dependent source signals and the novel NG measure is given by the Hall Euclidean distance. The proposed separation algorithm can result in the famous FastICA algorithm. Simulation results show that the proposed separation algorithm is able to separate the dependent signals and yield ideal performance.
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Wang, F., Li, H., Li, R. (2007). Novel NonGaussianity Measure Based BSS Algorithm for Dependent Signals. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_86
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DOI: https://doi.org/10.1007/978-3-540-72524-4_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72483-4
Online ISBN: 978-3-540-72524-4
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