The blind separation of non-stationary signals by only using the second order statistics

Lathauwer and Comon (see Signal Processing, vol.73, no.1-2, p.3-4, 1999) state that for the last 10 years, source separation has raised an increasing interest, partly because it has been discovered that space-time approaches will play an essential role in future radio communications. In the case of an instantaneous mixture (memoryless mixture or channel), many algorithms are proposed to solve the blind separation problem. In the general case (where no special assumption is assumed), the high order statistics (i.e. fourth order) are used. By adding special assumptions, algorithms and criteria can be simplified. In this paper, we discuss and present how the separation of a non-stationary signal can be done using only second order statistics.

[1]  Christian Jutten,et al.  Space or time adaptive signal processing by neural network models , 1987 .

[2]  S. Hochreiter,et al.  Lococode Performs Nonlinear ICA Without Knowing The Number Of Sources , 1999 .

[3]  Philippe Loubaton,et al.  Adaptive subspace algorithm for blind separation of independent sources in convolutive mixture , 2000, IEEE Trans. Signal Process..

[4]  Christian Jutten,et al.  A direct solution for blind separation of sources , 1996, IEEE Trans. Signal Process..

[5]  Christian Jutten,et al.  Detection de grandeurs primitives dans un message composite par une architecture de calcul neuromime , 1985 .

[6]  Philippe Garat,et al.  Blind separation of mixture of independent sources through a quasi-maximum likelihood approach , 1997, IEEE Trans. Signal Process..

[7]  S. Amari,et al.  Natural Gradient Approach To Blind Separation Of Over- And Under-Complete Mixtures , 1999 .

[8]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[9]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[11]  Nathalie Delfosse,et al.  Adaptive blind separation of independent sources: A deflation approach , 1995, Signal Process..

[12]  Blind source separation and multichannel deconvolution , 1999 .

[13]  Christian Jutten,et al.  Fourth-order criteria for blind sources separation , 1995, IEEE Trans. Signal Process..

[14]  A. Gorokhov,et al.  Subspace-based techniques for blind separation of convolutive mixtures with temporally correlated sources , 1997 .

[15]  É. Moulines,et al.  Second Order Blind Separation of Temporally Correlated Sources , 1993 .

[16]  C. Jutten,et al.  Subspace method for blind separation of sources and for a convolutive mixture model , 1996 .

[17]  Philippe Loubaton,et al.  Subspace based techniques for second order blind separation of convolutive mixtures with temporally correlated sources , 1997 .

[18]  W. Kofman,et al.  Source separation using higher order statistics , 1992 .

[19]  Dinh Tuan Pham,et al.  Separation of a mixture of independent sources through a maximum likelihood approach , 1992 .

[20]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[21]  Kiyotoshi Matsuoka,et al.  A neural net for blind separation of nonstationary signals , 1995, Neural Networks.