摘要
针对稀疏信号盲源分离势函数法需要过多参数,以及聚类算法需要知道源信号个数的缺陷,采用基于拉普拉斯模型的势函数法估计源信号数目和混合矩阵。将混合信号重新聚类,对每一类信号的协方差矩阵进行奇异值分解,混合矩阵得到更精确的估计,进而源信号也得到更精确的估计。通过计算机仿真,表明了该算法的优越性。
For the defects that blind source separation potential function method requires too many parameters and the number of the source signal needs to be known as priori condition in the clustering algorithm, the potential function method based on Lapla- cian model is used to estimate the number of source signals and the mixing matrix. Then the mixed signals are re-clustered, and the covariance matrix of each type of signal is solved with the singular value decomposition. The mixing matrix is estimated more precisely, and then the source signals are also estimated more precisely. Through computer simulation, it demonstrates the superiority of the proposed algorithm.
出处
《计算机工程与应用》
CSCD
2013年第14期212-216,共5页
Computer Engineering and Applications
基金
国家自然科学基金(No.60972084)
广西自然科学基金(No.0832007Z)
关键词
奇异值分解
混合矩阵
稀疏信号
势函数
聚类
singular value decomposition
mixing matrix
sparse signal
potential function
clustering
