基于改进蜂群聚类的欠定盲源分离

Underdetermined blind source separation based on improved bee colony clustering

针对欠定盲分离中混合矩阵估计精度不高的问题,采用了改进的人工蜂群(ABC)聚类算法。从观测信号的线性聚类特点和蜂群的多样性考虑,改进雇佣蜂的搜索策略,从而加快算法的收敛速度。同时,引入基于Levy飞行的局部搜索方法,进一步对当前最优解的邻域进行搜索,提高ABC算法局部开发能力。仿真结果表明,该方法在源个数较多的情况下仍然有较高的混合矩阵估计精度。

To solve the problem that the estimation accuracy of the mixing matrix in the underdetermined blind separation is not high, this paper proposes an improved Artificial Bee Colony（ABC）clustering algorithm. From the linear clustering characteristics of the observed signals and the diversity of bee colony, this paper improves the search strategy of the employed bee in order to speed up the convergence rate of the algorithm. At the same time, the local search method based on Levy flight is introduced to search the neighborhood of the current optimal solution, which can improve the local development ability of ABC algorithm. The simulation results show that the proposed method has higher estimation accuracy under the condition that the number of sources is large.