Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems

In this paper we discuss the semi parametric statistical model for blind deconvolution. First we introduce a Lie Group to the manifold of noncausal FIR filters. Then blind deconvolution problem is formulated in the framework of a semiparametric model, and a family of estimating functions is derived for blind deconvolution. A natural gradient learning algorithm is developed for training noncausal filters. Stability of the natural gradient algorithm is also analyzed in this framework.

[1]  P. Bickel Efficient and Adaptive Estimation for Semiparametric Models , 1993 .

[2]  Shun-ichi Amari,et al.  Adaptive blind signal processing-neural network approaches , 1998, Proc. IEEE.

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

[4]  Andrzej Cichocki,et al.  Multichannel blind deconvolution of non-minimum phase systems using information backpropagation , 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).

[5]  S. Amari,et al.  Geometrical structures of FIR manifold and their application to multichannel blind deconvolution , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[6]  S. Amari,et al.  Estimating Functions in Semiparametric Statistical Models , 1997 .

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

[8]  Shun-ichi Amari,et al.  Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information , 1997, Neural Computation.

[9]  Shun-ichi Amari,et al.  Blind source separation-semiparametric statistical approach , 1997, IEEE Trans. Signal Process..

[10]  Shun-ichi Amari,et al.  Superefficiency in blind source separation , 1999, IEEE Trans. Signal Process..

[11]  S.C. Douglas,et al.  Multichannel blind deconvolution and equalization using the natural gradient , 1997, First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.

[12]  K. Do,et al.  Efficient and Adaptive Estimation for Semiparametric Models. , 1994 .

[13]  T. Ens,et al.  Blind signal separation : statistical principles , 1998 .

[14]  F. Götze Differential-geometrical methods in statistics. Lecture notes in statistics - A. Shun-ichi. , 1987 .

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

[16]  Andrzej Cichocki,et al.  Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation , 2000, Neural Computation.

[17]  Shun-ichi Amari,et al.  Differential-geometrical methods in statistics , 1985 .

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

[19]  S. Amari,et al.  Estimation in the Presence of Infinitely many Nuisance Parameters--Geometry of Estimating Functions , 1988 .

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