Bayesian blind marginal separation of convolutively mixed discrete sources

Author

Andrieu, Christophe ; Doucet, Arnaud ; Godsill, Simon

Author_Institution

Dept. of Eng., Cambridge Univ., UK

fYear

1998

fDate

31 Aug-2 Sep 1998

Firstpage

43

Lastpage

52

Abstract

We formulate the discrete source separation problem in a Bayesian framework. We show that it is possible to integrate analytically the so called nuisance parameters, leading to an analytic expression of the marginal posterior distribution of the symbols conditional upon the observations. We present two algorithms, a deterministic algorithm and stochastic algorithm, that allow one to optimize the marginal posterior distribution. We present simulation results and draw some conclusions

Keywords

Bayes methods; convolution; optimisation; probability; signal detection; stochastic processes; Bayesian blind marginal separation; blind source separation; convolution; deterministic algorithm; marginal posterior distribution; mixed discrete sources; nuisance parameters; optimization; probability; stochastic algorithm; Bayesian methods; Convergence; Frequency conversion; GSM; Mobile communication; Multiaccess communication; Signal processing; Signal processing algorithms; Source separation; Stochastic processes;

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.1998.710631

Filename

710631