A new blind source separation method based on fractional lower-order statistics

摘要

We proposed neural network structures related to multilayer feed-forward networks for performing blind source separation (BSS) based on fractional lower-order statistics. As alpha stable distribution process has no its second- or higher-order statistics, we modified conventional BSS algorithms so that their capabilities are greatly improved under both Gaussian and lower-order alpha stable distribution noise environments. We analysed the performances of the new algorithm, including the stability and convergence performance. The analysis is based on the assumption that the additive noise can be modelled as alpha stable process. The simulation experiments and analysis show that the proposed class of networks and algorithms is more robust than second-order-statistics-based algorithm.