Abstract
This work derives and evaluates a method for Blind Source Extraction (BSE) in a reproducing kernel Hilbert space (RKHS) framework. The a priori information about the autocorrelation function of the signal under study is translated in a linear transformation of the Gram matrix of the transformed data in Hilbert space. Our method proved to be more robust than methods presented in the literature of BSE with respect to ambiguities in the available a priori information of the signal to be extracted. The approach here introduced can also be seen as a generalization of Kernel principal component analysis (KPCA) to analyze autocorrelation matrices at specific time lags.
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