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 of channel/equalizer cascade model which involves the definition of deconvolution noise. In this paper we consider four 'Bussgang' blind deconvolution algorithms for uniformly distributed source signals and investigate their numerical performances as well as some of their analytical features. Particularly, we show that the algorithm, introduced by the present author, provided by a flexible (neuromorphic) estimator is effective as it does not require to make any hypothesis about convolutional noise level and exhibits satisfactory numerical performances. (C) 2001 Elsevier Science B.V. All rights reserved.