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 deened on the basis of channel/equalizer cascade model which involves the deenition 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 their analytical features. Particularly, we show that the algorithm, introduced by the present author, provided by a exible (neuromorphic) estimator is eeec-tive as it does not require to make any hypothesis about convolutional noise level and exhibits satisfactory numerical performances.