In this paper we present a method for blind deconvolution of linear channels based on source separation techniques, for real word signals. This technique applied to blind deconvolution problems is based in exploiting not the spatial independence between signals but the temporal independence between samples of the signal. Our objective is to minimize the mutual information between samples of the...

Seismic deconvolution is a general problem associated with recovering the reflectivity series from a seismic signal when the wavelet is known. In this paper, we solve the problem of semi-blind seismic deconvolution, where the wavelet is known up to some error. The Multichannel Semi-blind Deconvolution (MSBD) model was developed for cases where there is some uncertainty in the assumed wavelet. W...

We present an estimator of the entropy of a signal. The basic idea is to adopt a model of the probability law, in the form of an AR spectrum. Then, the law parameters can be estimated from the data. We examine the statistical behavior of our estimates of laws and entropy. Finally, we give several examples of applications: an adaptive version of our entropy estimator is applied to detection of l...

`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...

‘Bussgang’ deconvolution techniques for blind digital channels equalization rely on a Bayesian estimator of the source sequence de1ned on the basis of channel=equalizer cascade model which involves the de1nition of deconvolution noise. In this paper we consider four ‘Bussgang’ blind deconvolution algorithms for uniformly distributed source signals and investigate their numerical performances as...