Abstract
Diffusion mixing estimator (DME)-based non-parametric blind source separation (BSS) algorithm is proposed under the framework of non-Bayesian framework and natural gradient optimisation method. In order 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 DME with a new data-driven bandwidth selection method can improve the performance of FKDE, which is inspired via a Langevin diffusion process. Moreover, the direct estimation of the score functions can separate the hybrid mixtures of sources that contain both symmetric and asymmetric distribution source signals and do not need to assume the parametric non-linear functions as them. The effectiveness of the proposed algorithm has been confirmed by simulation experiments.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Modelling, Identification and Control
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
