基于阵列平均分数阶傅里叶变换的盲源分离

Lu-ping Zhou, Bingrong Li, Chun-feng Wang
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引用次数: 0

摘要

提出了一种基于阵列平均分数阶傅里叶变换的盲源分离方法。该方法可以降低噪声水平,抑制源信号的相互作用,从而获得更好的分离性能。与以往基于时频分布的盲源分离技术相比,该方法产生的交叉项较小,且不需要白化、联合对角化和双线性信号合成。通过仿真验证了该方法的有效性。
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Blind source separation based on the array-averaged Fractional Fourier Transform
In this paper, a novel blind source separation method based on the array-averaged Fractional Fourier Transform is proposed. This method can decrease the noise levels, and suppress the interactions of the source signals, which lead to better separation performance. Compared with the previous blind source separation techniques based on the time-frequency distributions, this proposed method produces little crossterms, and it does not require whitening, joint-diagonalization, and bilinear signal synthesis. The improved efficiency of the method is verified by the simulation.
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