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In this paper we propose a new cost function for blind equalization which aims at forcing a given probability density at the output of the equalizer. In previous works based on this idea, the Kullback-Leibler distance was used as an appropriate measure of the distance between densities. Here we consider the Euclidean (quadratic) distance between the current pdf at the output of the equalizer and the...
Blind techniques attract the attention of many researchers due to their numerous promising applications in different fields of signal processing, from communications to control systems. Blind deconvolution is a problem that has been investigated in detail over the last two decades. Many approaches adopting various optimization criteria have been proposed to determine the inverse of the unknown channel...
A well-known fact in blind deconvolution is that if the unknown source signal is white (temporally) and the unknown channel filter is minimum phase, it is possible to determine the inverse filter (equalizer) by evaluating simply the power spectral density (PSD) of the received signal. For blind source separation, however, a similar special case, equivalent to the situation in blind deconvolution,...
Principal Components Analysis (PCA) is a very important statistical tool in signal processing, which has found successful applications in numerous engineering problems as well as other fields. In general, an on-line algorithm to adapt the PCA network to determine the principal projections of the input data is desired. The authors have recently introduced a fast, robust, and efficient PCA algorithm...
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