基于时频单源点检测和聚类验证技术的欠定混合盲辨识算法

针对欠定混合盲辨识问题,提出了一种基于时频单源点检测及聚类验证的盲辨识算法。检测各个源信号的时频单源点,利用奇异值分解的方法求解不同单源点集合对应的混合矢量,利用基于k均值的聚类验证技术完成源信号数目和混合矩阵的联合估计。算法放宽了已有方法对时频单源区域的假设,不需要假设信号存在时频单源区域,可以完成仅存在离散的时频单源点条件下的欠定混合盲辨识;同时克服了传统算法需要假设源信号个数已知的不足,可以有效地估计源信号数目。仿真结果验证了算法的有效性。

Blind Identification of Underdetermined Mixtures Based on Detection of Time Frequency Single Source Point and Cluster Validation Technique

Aiming at the problem of blind identification of underdetermined mixtures, this paper proposes an underdetermined blind identification algorithm based on the detection of time-frequency single source point and cluster validation technique. Firstly, detect single source point of each source signal, then estimate the mixing vector in the corresponding single source point set by Singular Value Decomposition (SVD), finally estimate the number of the source signals and the mixing matrix simultaneously by the cluster validation technique based on k-means clustering algorithm. Compared with the conventional algorithms with single source hypothesis, the proposed algorithm relaxes the sparsity requirement of the source signals and can estimate the mixing matrix under the assumption that there exist disjointed single source points for each source signal. Meanwhile, the proposed algorithm can estimate the number of the source signals while the conventional algorithms require it to be known as a priori. Simulation results indicate the efficiency of the proposed algorithm.