一种具有自动聚类检测功能的欠定型盲信号混叠矩阵估计算法

针对源信号个数未知情况下的欠定稀疏分量分析模型,提出一种具有自动聚类检测功能的混叠矩阵估计算法。提出实现源信号个数的判定的观测信号自动检测聚类方法,同时利用主成分分析对超直线进行估计,从而实现混叠矩阵的精确估计。仿真实验结果表明,该算法适用范围广,是一种快速精确且稳健的混叠矩阵估计算法。

A New Matrix Clustering Algorithm with Auto Detection for Underdetermined Sparse Component Analysis

To the underdetermined sparse component analysis (SCA) model with unknown sources number, a new robust clustering algorithm with auto detect function for mixture matrix estimation is addressed in this paper. This approach consists of two parts: signal clustering and mixing matrix estimation. In the first step, we propose a probability criterion for sources number detection, which stems from deduction by using a fit mathematical statistics model. To the second stage, Principal Component Analysis (PCA) is introduced to the mixing matrix estimation. Experiment simulations illustrate the effectiveness of the new clustering algorithm.