KuicNet algorithms for blind deconvolution

Author

Douglas, Scott C. ; Kung, S.Y.

Author_Institution

Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA

fYear

1998

fDate

31 Aug-2 Sep 1998

Firstpage

3

Lastpage

12

Abstract

We show how the recently-developed KuicNet method for instantaneous blind source separation can be extended to the blind deconvolution task. The proposed algorithm has a simple form and is effective in deconvolving source signals with non-zero kurtoses from a linear filtered version of the source sequence. We then combine the natural gradient search technique with the KuicNet algorithm to enhance its convergence properties. Simulations verify the useful behavior of the proposed algorithms in deconvolving sources with various distributions

Keywords

FIR filters; convergence of numerical methods; deconvolution; filtering theory; neural nets; optimisation; search problems; signal detection; telecommunication channels; FIR filtering; KuicNet algorithm; blind deconvolution; blind source separation; convergence; gradient search; kurtosis signals; optimisation; Blind equalizers; Blind source separation; Convergence; Convolution; Deconvolution; Finite impulse response filter; Nonlinear filters; Principal component analysis; Random variables; USA Councils;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop

Conference_Location

Cambridge

ISSN

1089-3555

Print_ISBN

0-7803-5060-X

Type

conf

DOI

10.1109/NNSP.1998.710621

Filename

710621