基于方向性模糊C-means与K-means的混合矩阵估计方法  

Mixing Matrix Estimation Based on Directional Fuzzy C-means and K-means

作　　者：黄宇扬 初萍 廖斌[1] HUANG Yuyang;CHU Ping;LIAO Bin(Guangdong Key Laboratory of Intelligent Information Processing,Shenzhen University,Shenzhen,Guangdong 518060,China)

机构地区：[1]深圳大学广东省智能信息处理重点实验室,广东深圳518060

出　　处：《信号处理》2021年第7期1295-1303,共9页Journal of Signal Processing

基　　金：国家自然科学基金(61771316);广东省自然科学基金(2020A1515010410)。

摘　　要：在信源数目未知的欠定盲源分离问题中,精确地估计混合矩阵是具有挑战性的问题。针对现有方法在病态条件下(某些混合向量的方向接近)不能准确估计信源数目、易受离群点干扰的不足,提出了一种基于方向性模糊C-means与K-means的混合矩阵估计方法。该方法首先通过方向性模糊C-means对观测信号进行预聚类,通过预聚类可以实现:1)根据聚类有效性指标值的收敛点确定信源数目;2)根据隶属度矩阵排除离群点;3)确定K-means的初始聚类点。最后使用K-means并利用预聚类确定的信源数目及初始聚类点实现混合矩阵估计。仿真结果表明提出的方法具有更优的混合矩阵估计性能。In underdetermined blind source separation with unknown number of sources,it is challenging to estimate the mixing matrix precisely.Two major shortcomings of existing methods are that they cannot estimate the number of sources correctly under ill-conditioned conditions(namely,the directions of some mixed vectors are close)and they are susceptible to outliers.To deal with these issues,a mixing matrix estimation method based on directional fuzzy C-means(DFCM)and K-means is proposed.First,the observation signals are pre-clustered by DFCM,such that we can:1)determine the number of sources according to the convergence point of the clustering validity index;2)eliminate outliers according to the membership matrix;3)determine the initial clustering points of K-means.Finally,K-means is used to achieve mixing matrix estimation based on the pre-determined number of sources and the initial clustering points.The simulation results suggest that the proposed method has superior performance of mixing matrix estimation.

关 键 词：盲源分离 混合矩阵估计 聚类 方向性模糊C-means K-MEANS 

分 类 号：TN911.7[电子电信—通信与信息系统]