Abstract
Abstract In the signal processing area, blind source separation(BSS) is a method aiming to recover independent sources from their linear instantaneous mixtures without resorting to any prior knowledge, such as mixing matrices and sources. Recent years have seen increased attention given to blind source separation in many areas, including wireless communication, biomedical imaging processing, multi-microphone array processing, and so on. In this thesis, we propose a new simple BSS technique that exploits second order statistics for non-stationary sources. Our technique utilizes the algebraic structure of the signal model and the subspace structures so as to efficiently recover sources with interference of noise. Computer simulations have demonstrated that, in comparison with other existent methods, our method has better performance in the regimes of low and medium SNRs. At high SNRs, our method is not as promising methods such as the method called AC("alternating columns")-DC("diagonal centers") algorithm, but it gives reasonable performance.