BLIND SOURCE EXTRACTION IN GAUSSIAN NOISE
Sergio Crucesy , Andrzej Cichockix, Luis Castedoz, x Lab. for Open Information Systems, Brain Science Institute, RIKEN, Japan. E-mail: cia@brain.riken.go.jp, zElectronic and Systems, University of La Coruņa, Spain. E-mail: luis@sol.des. .udc.es
ySignal Processing Group, University of Seville, Spain. E-mail: sergio@viento.us.es, x Lab. for Open Information Systems, Brain Science Institute, RIKEN, Japan. E-mail: cia@brain.riken.go.jp, zElectronic and Systems, University of La Coruņa, Spain. E-mail: luis@sol.des. .udc.es
In this paper we address the problem of recovering a
subset of sources from a noisy linear mixture. We pro-
pose a novel blind source extraction algorithm that is
robust with respect to the noise. This robustness is
two-fold: on the one hand the algorithm does not lead
to biassed estimates and, on the other, it minimizes
the amount of signal and noise interference on the es-
timated sources. In addition, the asymptotic conver-
gence of the algorithm to the extraction solution is
demonstrated for almost any source distribution. Fi-
nally, the proposed algorithm is shown to be a gener-
alization of the powerful kurtosis FAST-ICA algorithm
that enables us to sequentially extract the sources in
an arbitrary number of groups.