An on-line adaptive blind source separation algorithm for
the separation of convolutive mixtures of cyclostationary
source signals is proposed. The algorithm is derived by applying natural gradient iterative learning to the novel cost
function which is defined according to the wide sense cyclostationarity
of signals. The efficiency of the algorithm
is supported by simulations, which show that the proposed
algorithm has improved performance for the separation of
convolved cyclostationary signals in terms of convergence
speed and waveform similarity measurement, as compared
to the conventional natural gradient algorithm for convolutive
mixtures