A robust H∞ learning approach to blind separation of signals

Authors:
Niva Das;Aurobinda Routray;Pradipta Kishore Dash
Affiliations:
Institute of Technical Education and Research, Department of Electronics and Telecommunication Engineering, BBSR, Orissa, India;Indian Institute of Technology, Kharagpur, West Bengal, India;Silicon Institute of Technology, BBSR, Orissa, India
Venue:
Digital Signal Processing
Year:
2010

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Abstract

A robust estimation technique based on the H"~ filter (learning) is proposed in this paper to address the instantaneous Blind source separation (BSS) problem in a non-stationary mixing environment. It is assumed that the variations in the mixing system are small. The learning algorithm is obtained by applying H"~ filter to the BSS model with state-space representation. The motivation behind applying H"~ filter is its robustness towards errors arising out of model uncertainties, parameter variations and noise. The proposed algorithm is applied to both synthetically generated signals and practical sound signals. A performance comparison between the H"~ filter, Kalman filter, ICA based on mutual information and Nonlinear PCA establishes the robustness of the proposed H"~ approach.