A contribution to (neuromorphic) blind deconvolution by flexible approximated Bayesian estimation

'Bussgang' deconvolution techniques for blind digital channels equalization rely on a Bayesian estimator of the source sequence defined on the basis o f channel/equalizer cascade model which involves the definition of deconvol ution noise. In this paper we consider four 'Bussgang' blind deconvolution algorithms for uniformly distributed source signals and investigate their n umerical performances as well as some of their analytical features. Particu larly, we show that the algorithm, introduced by the present author, provid ed by a flexible (neuromorphic) estimator is effective as it does not requi re to make any hypothesis about convolutional noise level and exhibits sati sfactory numerical performances. (C) 2001 Elsevier Science B.V. All rights reserved.