Blind Source Separation Based on Dual Adaptive Control
نویسندگان
چکیده
ABSTRACT This paper presents a new method for Blind Source Separation (BSS) based on dual adaptive control, which allows successful separation of linear mixtures of independent source signals. The method reformulates a BSS problem to get a dual adaptive control problem. Then a Sigmoid MLP neural network is used to approximate the widesence-mixing matrix defined in the BSS problem. By solving the dual adaptive control problem, in which unknown parameters of the neural network are estimated by applying the Extended Kalman Filter, we then obtain the widesense-mixing matrix. Experimental results show that individual source signals can be separated effectively from the known linear mixture signals using this method. And faster convergence speed as well as good performance can be achieved.
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تاریخ انتشار 2000