基于相关矩阵对角化和遗传算法的盲源分离法

Blind source separation method based on diagonalization of correlation matrices and genetic algorithm

提出了一种新的基于相关矩阵对角化的代价函数作为衡量输出信号独立性的测度。为了扩大搜索空间,降低各信源之间的互相关性,将代价函数进行了非线性变换。还提出了利用实数编码的遗传算法对代价函数进行最优化搜索,以克服传统梯度搜索方法容易陷入局部收敛的缺点。此方法不仅适用于平稳或非平稳信号,而且还可用于瞬时或卷积混和模型的盲源分离问题。仿真实验表明,该算法具有快速收敛性能和高精确度等优点,能够大大提高分离后的输出信噪比。

A new cost function based on the diagonalization of correlation matrices is proposed to measure the independency of output signals. In order to expand the search space and decrease the crosscorrelation among the subsources, non-linear transform is made for the cost function. The real coded genetic algorithm is also proposed to search for the optimum solution, which can overcome the drawback of traditional gradient search technique that it is likely to tend to faU into the local minimum. This novel method is applicable to instantaneous or convolutive mixture models with stationary or non-stationary input signals. Simulation results demonstrate that the algorithm not only has fast convergence and high accuracy, but also can improve the output SNR greatly.