摘要
本文将孤立点检测的思想引入到欠定混合矩阵的盲辨识问题,提出了一种基于孤立点检测的混合矩阵盲辨识方法.首先计算混合信号的空间时频分布并检测出单源时频点,然后检测出单源时频点中的孤立点并将其从中去除,再通过聚类的方法估计混合矩阵.该方法降低了对信号稀疏性的要求,通过去除数据中的孤立点,提高了矩阵的估计精度,同时也有助于对源信号数目的估计.仿真实验表明,与已有算法相比,本文方法进一步提高了混合矩阵的估计精度,并且有更强的鲁棒性.
This paper introduces the concept of outlier detection into blind identification of underdetermined mixtures. We propose a mixing matrix estimation algorithm based on outlier detection. First calculate the spatial Time-Frequency （TF） distribution of the mixtures,detect the single source points in the TF domain, and then detect the outliers, remove them from the set of single source points,and finally estimate the mixing matrix using a clustering method. The proposed algorithm relaxes the condition on the sparsity of sources. The mixing matrix estimation accuracy is improved by detecling the outliers and removing them, which is also helpful for the estimation of the number of sources. Simulation results show that the proposed algorithm eslimates the mixing matrix with high accuracy and robusmess compared with other algorithms.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2012年第12期2367-2373,共7页
Acta Electronica Sinica
关键词
欠定盲辨识
单源时频点
孤立点检测
聚类
blind identification of underdetermined mixtures
single source points in the time-frequency domain
outlier de- tection
clustering
