人工蜂群优化算法在复数盲源分离中的应用

针对复值信号的源数估计和有序分离等关键技术,提出一种基于人工蜂群优化的源数未知的复值盲源分离方法,该方法首先利用交叉互验技术来估算复数源信号的个数,然后通过人工蜂群算法优化峰度的绝对值来获得最佳分离向量,并实现了逐次恢复源信号的目的.仿真实验结果表明,该方法不仅能依峰度绝对值的降序实现服从任何分布源信号的盲分离,同时比其他方法具有更优越的估计性能.另外,提出一种基于峰度的欠定复盲源分离算法,该算法根据信号的统计特性构造了用于欠定混合情况下盲抽取向量的代价函数,然后通过人工蜂群算法优化其函数来获得最佳分离向量,通过多次分离来实现欠定复盲源分离的目的.通过对混合分布类型的复值源信号欠定盲分离仿真实验验证了该算法的有效性.

Application of artificial bee colony optimization algorithm in complex blind separation source

Aiming at the key technologies of number estimation and ordered recovery of complex-valued signals, a method of complex blind source separation with an unknown number of sources based on artificial bee colony optimization was proposed. Firstly, we introduced an algorithm based on cross validation technology to estimate number of sources. Then, the optimally extracted vectors were determined through maximizing absolute value of kurtosis by using artificial bee colony optimization, so as to separate complex-valued signals one by one. The simulation results show that the proposed method can achieve the blind separation for source signals of any distribution in decreasing order of absolute kurtosis, and compared to other conventional algorithms, this method has preferable estimating performancee. In addition, we considered the underdetermined complex blind source separation problem of instantaneous mixtures based on kurtosis. The cost function of the extracted vector in the underdetermined mixed case was constructed by exploiting the statistics properties of complex-valued source signals, and then artificial bee colony optimization algorithm was used to maximize the function to determine the optimally extracted vectors. The underdetermined complex blind source separation was achieved through many times of extracting. The simulation result of blind separation of different types of signals validates the feasibility of the proposed method.