Multichannel blind signal adaptive separation algorithm based on polyspectra analysis

Separation of multiple signals from their superposition recorded at several sensors is addressed. The methods employ polyspectra of the sensor data in order to extract the unknown signals and estimate the finite impulse response (FIR) coupling systems via a linear equation based algorithm. The procedure is useful for multichannel blind deconvolution of colored input signals with (possibly) overlapping spectra. An extension of the main algorithm, which can be applied for quasiperiodic signal separation, is also given. Simulation results corroborate the applicability of the algorithm.

[1]  A. Ebrahimzadeh,et al.  A comparative study of bees colony algorithm for blind source separation , 2012, 20th Iranian Conference on Electrical Engineering (ICEE2012).

[2]  Georgios B. Giannakis,et al.  Modeling of non-Gaussian array data using cumulants: DOA estimation of more sources with less sensors , 1993, Signal Process..

[3]  Erkki Oja,et al.  Speed and Accuracy Enhancement of Linear ICA Techniques Using Rational Nonlinear Functions , 2007, ICA.

[4]  Tzyy-Ping Jung,et al.  Recursive independent component analysis for online blind source separation , 2012, 2012 IEEE International Symposium on Circuits and Systems.

[5]  Jimin Ye,et al.  Adaptive weighted orthogonal constrained algorithm for blind source separation , 2013, Digit. Signal Process..

[6]  Hao Shen,et al.  Blind Source Separation With Compressively Sensed Linear Mixtures , 2011, IEEE Signal Processing Letters.

[7]  Yang Wang,et al.  Source extraction in audio via background learning , 2013 .

[8]  Lang Tong,et al.  Waveform-preserving blind estimation of multiple independent sources , 1993, IEEE Trans. Signal Process..

[9]  Chen Zhong A Fast Algorithm of Blind Signal Separation Based on ICA , 2004 .

[10]  Namyong Kim,et al.  Blind signal processing for impulsive noise channels , 2012, Journal of Communications and Networks.

[11]  Lang Tong,et al.  Indeterminacy and identifiability of blind identification , 1991 .

[12]  Hui Tang,et al.  Noisy blind source separation based on adaptive noise removal , 2012, Proceedings of the 10th World Congress on Intelligent Control and Automation.

[13]  Eric Moreau,et al.  A robust algorithm for convolutive blind source separation in presence of noise , 2013, Signal Process..

[14]  Hualiang Li,et al.  Complex ICA Using Nonlinear Functions , 2008, IEEE Transactions on Signal Processing.

[15]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[16]  Hirokazu Kameoka,et al.  Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data , 2013, IEEE Transactions on Audio, Speech, and Language Processing.