Blind Source Separation Based on Non-Gaussianity

Bai Zhan Yang

doi:10.4028/www.scientific.net/AMR.532-533.1378

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533Blind Source Separation Based on Non-Gaussianity

Blind Source Separation Based on Non-Gaussianity

Abstract:

Independent component analysis is an efficient way to solve blind source separation, which has been broadly used in many fields, such as speech recognition, image processing, wireless communication system, biomedical signal processing etc. Independent component analysis for the traditional ways to solve the blind source separation problem only considers the non-Gaussian signal, without taking into account the time structure of the signal information. Proposed based on generalized self-related and non-Gaussian source separation method, the full account of the non-Gaussian signal and time structure information, to solve the blind source separation problem in the time structure of the signal. Finally, this simulation method is validated, the simulation results show that the method is effective and worthy of promotion.

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Periodical:

Advanced Materials Research (Volumes 532-533)

Pages:

1378-1383

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.532-533.1378

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Online since:

June 2012

Authors:

Bai Zhan Yang

Keywords:

Blind Source Separation, Independent Component Analysis (ICA), Non-Gaussianity, Time-Structure

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