基于改进K-均值聚类的欠定混合矩阵盲估计

在源信号个数未知条件下，提出一种基于改进K-均值聚类的欠定混合矩阵盲估计方法。该方法首先计算观测信号在单位半超球面上投影点的密度参数，然后去掉低密度投影点，并从高密度投影点中选取初始聚类中心，最后对剩余投影点进行聚类，根据Davies-Bouldin指标估计源信号个数，并估计出混合矩阵。仿真结果表明，该方法的复杂度低，其运行时间仅为拉普拉斯势函数法的1%~3%；该方法的源信号个数估计正确率远高于鲁棒竞争聚类算法，当信噪比高于13 dB时，该方法源信号个数估计正确率大于96.6%，且混合矩阵估计误差较小。该方法在信噪比较高时，可降低对源信号稀疏度的要求。

Blind estimation of underdetermined mixing matrix based on improved K-means clustering

A method for blind estimation of underdetermined mixing matrix based on improved K-means clustering is proposed when the source number is unknown. First, the density parameter of the projection points of the mixing signals on half of the unit ultra sphere is calculated. Then, the projection points with low density are removed and the initial clustering centers are chosen from the projection points with high density. Finally, cluster the remaining points, use the Davies Boudin index to estimate the source number, and estimate the mixing matrix. The simulation results show that the proposed algorithm’s complexity is lower and its running time is only about 1% to 3% of that of the Laplace mixed model potential function algorithm; its source number estimation accuracy is much higher than that of the robust competitive agglomeration algorithm; when the signal to noise ratio is greater than 13 dB, its accuracy is higher than 96.6% and its estimated mixing matrix error is small. When SNR is higher, it can relax the sparsity requirement of the sources.