Blind deconvolution/equalization using state-space models

We propose extension of multichannel blind equalization problem assuming that both mixing and demixing models are described by stable linear state-space systems. The problem is formulated as an optimization task. New learning algorithms are developed which can be considered as extension of existing algorithms. By applying demixing state-space model we are able to reduce the complexity of separating systems in some practical implementations.

[1]  P. Comon,et al.  Contrasts for multichannel blind deconvolution , 1996, IEEE Signal Processing Letters.

[2]  Fathi M. A. Salam,et al.  Algorithms for blind signal separation and recovery in static and dynamic environments , 1997, Proceedings of 1997 IEEE International Symposium on Circuits and Systems. Circuits and Systems in the Information Age ISCAS '97.

[3]  Shun-ichi Amari,et al.  Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.

[4]  Andrzej Cichocki,et al.  Robust neural networks with on-line learning for blind identification and blind separation of sources , 1996 .

[5]  Marimuthu Palaniswami,et al.  Computational Intelligence: A Dynamic System Perspective , 1995 .

[6]  Andrzej Cichocki,et al.  Stability Analysis of Learning Algorithms for Blind Source Separation , 1997, Neural Networks.

[7]  Christian Jutten,et al.  Blind source separation for convolutive mixtures , 1995, Signal Process..

[8]  Yujiro Inouye,et al.  Blind Deconvolution of Multichannel Linear Time-Invariant Systems of Nonminimum Phase , 2018, Statistical Methods in Control & Signal Processing.

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

[10]  Erkki Oja,et al.  Signal Separation by Nonlinear Hebbian Learning , 1995 .

[11]  Shun-ichi Amari,et al.  Adaptive approach to blind source separation with cancellation of additive and convolutional noise , 1996, Proceedings of Third International Conference on Signal Processing (ICSP'96).

[12]  Ehud Weinstein,et al.  Super-exponential methods for blind deconvolution , 1993, IEEE Trans. Inf. Theory.

[13]  Shun-ichi Amari,et al.  Stability Analysis Of Adaptive Blind Source Separation , 1997 .

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

[15]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[16]  E. Weinstein,et al.  Super-exponential methods for blind deconvolution , 1991, 17th Convention of Electrical and Electronics Engineers in Israel.

[17]  Shun-ichi Amari,et al.  Neural Network Models for Blind Separation of Time Delayed and Convolved Signals , 1997 .

[18]  Jitendra K. Tugnait,et al.  Blind equalization and channel estimation for multiple-input multiple-output communications systems , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

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

[20]  Te-Won Lee,et al.  Blind Separation of Delayed and Convolved Sources , 1996, NIPS.

[21]  J. Cardoso.  Infomax and maximum likelihood for blind source separation , 1997, IEEE Signal Processing Letters.

[22]  Andrzej Cichocki,et al.  Robust learning algorithm for blind separation of signals , 1994 .

[23]  Kari Torkkola,et al.  Blind separation of convolved sources based on information maximization , 1996, Neural Networks for Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop.

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

[25]  S. Amari.  Natural Gradient Works Eciently in Learning , 2022 .