A Comparative Study of Infomax, Extended Infomax and Multi-User Kurtosis Algorithms for Blind Source Separation

A Comparative Study of Infomax, Extended Infomax and Multi-User Kurtosis Algorithms for Blind Source Separation

Monorama Swaim (Silicon Institute of Technology, Bhubaneswar, IN), Rutuparna Panda (Veer Surendra Sai University of Technology, Odisha, IN) and Prithviraj Kabisatpathy (C. V. Raman College of Engineering, Odisha, IN)
Copyright: © 2019 |Pages: 17
DOI: 10.4018/IJRSDA.2019010101
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In this article for the separation of Super Gaussian and Sub-Gaussian signals, we have considered the Multi-User Kurtosis(MUK), Infomax (Information Maximization) and Extended Infomax algorithms. For Extended Infomax we have taken two different non-linear functions and new coefficients and for Infomax we have taken a single non-linear function. We have derived MUK algorithm with stochastic gradient update iteratively using MUK cost function abided by a Gram-Schmidt orthogonalization to project on to the criterion constraint. Amongst the various standards available for measuring blind source separation, Cross-correlation coefficient and Kurtosis are considered to analyze the performance of the algorithms. An important finding of this study, as is evident from the performance table, is that the Kurtosis and Correlation coefficient values are the most favorable for the Extended Infomax algorithm, when compared with the others.
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2. Methodology

We have passed signal S to the model which is statistically independent, then the signal mixed with matrix A to get result vector Y. After that matrix W is calculated using de-mixing (Routray & Kishore, 2007).

The BSS model as in equation (1) below



IJRSDA.2019010101.m02 transmitted Source Signals vector IJRSDA.2019010101.m03: is Mixing Matrix. IJRSDA.2019010101.m04: is a vector (acquired signal) IJRSDA.2019010101.m05: Vector with add-on noise samples at time instant IJRSDA.2019010101.m06IJRSDA.2019010101.m07 is Matrix / Transpose Vector
Figure 1.

System overview of the BSS process


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