非平稳信号自适应最大信噪比盲源分离方法

为了提高时变非平稳信号的盲源分离效果,提出了自适应最大信噪比盲源分离新方法.该方法以信噪比函数作为代价函数,并基于改进的多项式系数自回归模型,进行最优滑窗长度的自适应估计.仿真计算表明,FastICA算法需要预设源信号的概率密度函数,以选择适宜的非线性函数近似估计源信号的非高斯性,当假设的概率密度函数与实际不符时无法正确分离源信号;累积量算法在信源的峰度相同时无法正确分离源信号.新方法与经典的FastICA算法和基于累积量的盲源分离算法比较结果表明,对于经典的FastICA算法、累积量算法无法正确分离的时变非平稳信号,新方法能够有效地进行盲源分离,分离结果不受源信号的概率分布、信源的峰度等统计因素影响. 

Blind Sources Separation of Non-stationary Signals Based on Adaptive Maximum Signal-to-Noise Ratio Method

In order to improve the blind separation performance of non-stationary signals, a new blind source separation algorithm named adaptive maximum signal-to-noise ratio algorithm was proposed. This algorithm uses the signal noise ratio function as the cost function parameter and an improved multinomial coefficient autoregressive model to estimate the best length of moving average window. Simulations showed that FastICA algorithm needs to assume the probability density function (PDF) of the sources to approximate their un-Gaussian features by choosing the appropriate nonlinear function. If the assumed PDF considerably deviates from the true one, the sources could not be separated correctly. In the case of the sources with identical kurtosis, the separation algorithm using cumulants failed to separate the sources. The comparison between the proposed method, the classical FastICA algorithm, and the separation algorithm using cumulants showed that the proposed method could retrieve the time-varying non-stationary source signals accurately, and the separation performance of the proposed method was not influenced by the PDF and the kurtosis of the source signals.