No abstract

Blind source separation (BSS) consists of recovering the independent source signals from their linear mixtures with unknown mixing channel. As a momentous technical means of signal processing and analysis, BSS has been widely utilized in image recognition, feature extraction, biomedical engineering, etcetera. The existing BSS approaches rely on the fundamental assumption: the source signals are non-Gaussian, this limited the use of BSS seriously. To overcome this problem and the weakness of cosine index in measuring the dynamic similarity between signals, this study proposes the fuzzy statistical behavior of local extremum (FSBLE) based on generalized Jaccard similarity as the signal's feature to implement the separation of nonlinear chaotic Gaussian 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 data verify the effectiveness and superiority of the improved BSS approach in terms of cross-talking error and root mean square error (RMSE) criterions.