An improved blind Gaussian source separation approach based on generalized Jaccard similarity

Blind source separation (BSS) consists of recovering the independent source signals from their linear mixtures with unknown mixing channel. The existing BSS approaches rely on the fundamental assumption: the number of Gaussian source signals is no more than one, this limited the use of BSS seriously. To overcome this problem and the weakness of cosine index in measuring the dynamic similarity of signals, this study proposes the fuzzy statistical behavior of local extremum based on generalized Jaccard similarity as the measure of signal's similarity to implement the separation of source signals. In particular, the imperialist competition algorithm is introduced to minimize the cost function which jointly considers the stationarity factor describing the dynamical similarity of each source signal separately and the independency factor describing the dynamical similarity between source signals. Simulation experiments on synthetic nonlinear chaotic Gaussian data and ECG signals verify the effectiveness of the improved BSS approach and the relatively small cross-talking error and root mean square error indicate that the approach improves the accuracy of signal separation.