基于高阶统计量的自适应盲源分离算法

Blind Sources Separation Using Higher Order Statistic

提出了一种新的自适应盲源分离算法。在无噪音实时两源两传感器的情况下 ,一旦观测信号被白化 ,只需要辨识一个特定的旋转矩阵就可以完成盲源分离 ,并给出了能表征该旋转矩阵的角的自适应估计器。仿真结果表明 ,当满足源峭度和不为零的条件时 。

A new learning algorithm is developed for blind separation of independent source signals from their linear mixtures. In the noiseless real mixture two source two sensor scenario, once the observations are whitened (decorrelated and normalized), only a given rotation matrix remains to be identified in order to achieve the source separation. In this paper, an adapter estimator of the angle that characterizes such a rotation is derived. It shows that estimator converges to a stable valid separation solution with the only condition that the sum of source kurtosis be distinct from zero. Simulation demonstrate the validity of the algorithm.