Linear and Instantaneous Mixtures Following Eigenvalue Decomposition
Cheng Wang, Yongxiang Wu, Jin Gou, Gui-Rong Yan
Linear and Instantaneous Mixtures Following Eigenvalue Decomposition
Cheng Wang, Yongxiang Wu, Jin Gou, Gui-Rong Yan
Abstract :The physical significance of independent component analysis is not
clear, and it does not have only one solution. Aiming at the
disadvantages, a novel linear and instantaneous mixture algorithm
method based on eigenvalue decomposition of observation signals
autocorrelation matrix is proposed. Assumptions of this new method
are different from the traditional blind source separation model and
independent component analysis algorithm. For different types of
mixed matrix, this new method gets different information of source
signals. The numerical simulation results show that, it can
separate multiple unrelated source signals in despite of Gauss
measurement noise and can also achieve noise reduction effect after
separation and reconstruction of observation signals.
Key words :
Linear and Instantaneous Mixtures Problem
Unrelated Source Signals
Mixtures Matrix
Autocorrelation Matrix
Eigenvalue Decomposition
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