Blind extraction of cyclostationary signal from convolutional mixtures

Extracting a signal of interest from available measurements is a challenging problem. One property which can be utilized to extract the signal is cyclostationarity, which exists in many signals. Various blind source separation methods based on cyclostationarity have been reported in the literature but they assume that the mixing system is instantaneous. In this paper, we propose a method for blind extraction of cyclostationary signal from convolutional mixtures. Given that the signal of interest has a unique cyclostationary frequency and the sensors are placed close to the concerned signal, we show that the signal of interest can be estimated from the measured data. Simulations results show the effectiveness of our method.