未知源信号峭度正负性的盲分离

Blind separation with unknown signs of the kurtoses of sources

大多数的盲分离算法假设源信号峭度的正负性是已知的,并据此选择相应的非线性函数近似评价函数(scorefunction)。针对源信号峭度的正负性未知的情况,本文提出了一个评价函数的参数估计方法,本算法能有效地分离混合在一起的超高斯信号和亚高斯信号,仿真结果验证了算法的有效性。

Most blind source separation algorithms assume that the signs of the kurtoses of the source signals are known, according to which nonlinear functions are chosen to approximate the score functions. To tackle the cases where the signs of the kurtoses are unknown, we propose a new algorithm exploiting the parametric estimation of the score function. The proposed algorithm can separate super-Gaussian, Gaussian and sub-Gaussian signals from their mixtures. Validity and performance of the proposed algorithm are demonstrated by extensive computer simulations.