Blind source separation of nonlinear mixture from correlated sources based on Kernel Canonical Correlation Analysis
Based on the deficiency in the traditional blind separation method of statistically correlated sources, a new blind separation method of nonlinear mixture from correlated sources is proposed. In the proposed method, the nonlinear problem between the data can be processed by the kernel method, and the correlated sources can be effectively separated using the correlate of source signals. The proposed method is compared with traditional blind separation method of statistically correlated sources. The simulation results show that the proposed method is obviously superior to the traditional blind separation method of statistically correlated, and the performance index of separation can be reflected. Finally the proposed method is applied to blind separation of the misalignment of rotary and rotor rub-impact, the experiment results further validate the effectiveness of the proposed method.
Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang, 330063
Abstract：Based on the deficiency in the traditional blind separation method of statistically correlated sources, a new blind separation method of nonlinear mixture from correlated sources is proposed. In the proposed method, the nonlinear problem between the data can be processed by the kernel method, and the correlated sources can be effectively separated using the correlate of source signals. The proposed method is compared with traditional blind separation method of statistically correlated sources. The simulation results show that the proposed method is obviously superior to the traditional blind separation method of statistically correlated, and the performance index of separation can be reflected. Finally the proposed method is applied to blind separation of the misalignment of rotary and rotor rub-impact, the experiment results further validate the effectiveness of the proposed method.
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