基于实数编码遗传算法的盲信源分离方法

Blind Source Separation Method Based on Real Coded Genetic Algorithm

提出了一种新的基于相关矩阵对角化的代价函数,该代价函数通过抑制分离信号的互相关性达到盲信源分离的目的。这种分离新方法可用于分离平稳或非平稳信号的瞬时或卷积混合。针对传统梯度搜索方法容易陷入局部收敛的问题,文章还提出利用实数编码遗传算法对代价函数进行最优化搜索。仿真实验表明,这种遗传算法具有快速收敛性能和高精确度等优点。

In this paper, a new cost function based on the diagonalization of correlation matrices is proposed to perform blind source separation. This cost function can restrain cross-correlation of separated signals and be applicable to separate instantaneous or convolutive mixture of stationary or non-stationary signals. A real coded genetic algorithm is proposed to search the optimum solution. Computer simulation results demonstrate this algorithm has not only fast convergence performance but also high accuracy.