基于品质函数估计的非线性盲源分离算法

研究非线性盲源信号分离优化问题。由于混合信号同时包含超高斯和亚高斯信号且混合信号具有很强的非线性时,传统的非线性盲源分离算法中对于品质函数的选取一般都是通过经验,现有算法难以取得理想的分离效果。在Pearson模型的基础上提出了一种新的估计品质函数的方法,算法能够成功地估计出次高斯(sub-Gaussian)和超高斯(super-Gaussi-an)混合信号的品质函数,同时克服了Pearson模型对同类信号只能估计得到相同的品质函数的缺陷,提高了算法的估计精度。通过在MATLAB仿真验证了算法的可行性和有效性,成功估计出源信号的品质函数且实现了非线性盲源分离。

Algorithm of Nonlinear Blind Source Separation Based on Score Function Estimation

In traditional methods of nonlinear source separation,the score function is chosen empirically.The performance of the present nonlinear source separation algorithm is degraded when the mixed signals contain super-Gaussian and sub-Gaussian signals and the nonlinear distortion is serious.In this paper,the proposed score function was derived from the Pearson model and can efficiently approximate the sub-Gaussian and super-Gaussian signal.The proposed algorithm overcome the defects that Pearson gets the same score functions through estimating the same kind of signals(e.g.sub-Gaussian signals),and improved the precision of the estimation of the score function.We performed simulations based on MATLAB and the experimental results demonstrate that the proposed method is effective and efficient.And the algorithm successfully estimates the score function and separates the nonlinear mixing signals.