Blind source-separation using second-order cyclostationarystatistics
IEEE Transactions on Signal Processing
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Cyclostationary signals can be met in various domains, such as telecomunications and vibration analysis. Cyclostationarity allows to model repetitive signals and hidden periodicities such as those induced by modulation for communications and by rotating machines for vibrations. In some cases, the fundamental frequency of these repetitive phenomena can be known. The algorithm that we propose aims at extracting one cyclostationary source, whose cyclic frequency is a priori known, from a set of observations. We propose a new criterion based on second order statistics of the measures which is easy to estimate and leads to extraction with very good accuracy.