  This paper deals with a source separation strategy based on second-order
statistics, namely, on data covariance matrices estimated at several lags. In
general, ``blind'' approaches to source separation do not assume any knowledge
on the mixing operator; however, any prior information about the possible
structure of the mixing operator can improve the solution. Unlike ICA blind
separation approaches, where mutual independence between the sources is
assumed, our method only needs to constrain second-order statistics, and is
effective even if the original sources are significantly correlated. Besides
the mixing matrix, our strategy is also capable to evaluate the source
covariance functions at several lags. Moreover, once the mixing parameters have
been identified, a simple deconvolution can be used to estimate the probability
density functions of the source processes. To benchmark our algorithm, we used
a database that simulates the one expected from the instruments that will
operate onboard ESA's Planck Surveyor Satellite to measure the CMB anisotropies
all over the celestial sphere. The assumption was made that the emission
spectra of the galactic foregrounds can be parametrised, thus reducing the
number of unknowns for system identification to the number of the foreground
radiations. We performed separation in several sky patches, featuring different
levels of galactic contamination to the CMB, and assuming several noise levels,
including the ones derived from the Planck specifications.
