基于信号时频域聚集性的欠定盲分离混合矩阵估计方法

为解决欠定盲分离中混合矩阵估计问题，通过研究观测信号在时频域的线性聚集特性，提出一种基于时频域线性聚集程度差异的混合矩阵估计方法，并着重研究在信号线性聚集程度较弱情况下对混合矩阵的估计.首先，利用观测信号或其时频域中相应变换系数的比值分布衡量信号线性聚集程度；其次，采用优化初始中心的K-均值聚类算法估计混合矩阵.该算法降低了对信号稀疏性的要求，并且可以较高精度地估计出混合矩阵.仿真实验结果表明该方法具有可行性和有效性. 

Mixing Matrix Estimation Based on Cluster Degree of Time-Frequency Signal for Underdetermined Blind Source Separation

To solve the mixing matrix estimation problem of underdetermined blind separation, through the study of signal linear aggregation feature in time-frequency domain, an estimation method of mixing matrix based on different signal linear aggregation degree in time-frequency domain was proposed in this paper, and focus on the estimation of mixing matrix under the signal linear aggregation degree in weaker conditions. First, the observed signal or the ratio distribution of the corresponding transformation coefficient in time-frequency domain was used to measure the degree of the signal linear aggregation; second, the improved K-means clustering algorithm was applied to estimate the mixing matrix. The proposed method reduces the requirement for signal sparsity and can estimate the mixing matrix accurately. The simulation results show that the proposed method is feasible and effective.