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

Monorama Swaim, Rutuparna Panda, Prithviraj Kabisatpathy

Source Title: International Journal of Rough Sets and Data Analysis (IJRSDA) 6(1)

DOI: 10.4018/IJRSDA.2019010101

Article PDF Download Open access articles are freely available for download

Abstract

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