Non-parametric generalized cross entropy estimator for bss algorithm

Generalized cross entropy estimator (GCEE)-based non-parametric blind signal separation (BSS) algorithm is proposed under the framework of natural gradient (NG) optimization method. To improve the performance of signal separation by BSS, the probability distribution of source signals must be described as accurately as possible. Compared to the non-parametric fixed-width kernel density estimator (FKDE) method, the GCEE with a new datadriven bandwidth selection method can improve the performance of FKDE, which is inspired by the principles of the generalized cross entropy method. Moreover, the direct estimation of the score functions can separate the hybrid mixture of sources that contain both symmetric and asymmetric distribution source signals and do not need to assume the parametric non-linear functions of them. The effectiveness of the proposed algorithm has been confirmed by simulation experiments.