BLIND SOURCE SEPARATION BASED ON THE FRACTIONAL FOURIER TRANSFORM

Different approaches have been suggested in recent years to the blind source separation problem, in which a set of signals is recovered out of its instantaneous linear mixture. Many widely-used algorithms are based on second-order statistics, and some of these algorithms are based on timefrequency analysis. In this paper we set a general framework for this family of second-order statistics based algorithms, and identify some of these algorithms as special cases in that framework. We further suggest a new algorithm that is based on the fractional Fourier transform (FRT), and is suited to handle non-stationary signals. The FRT is a tool widely used in time-frequency analysis and therefore takes a considerable place in the signalprocessing field. In contrast to other blind source separation algorithms suited for the non-stationary case, our algorithm has two major advantages: it does not require the assumption that the signals’ powers vary over time, and it does not require a pre-processing stage for selecting the points in the time-frequency plane to be considered. We demonstrate the performance of the algorithm using simulation results.