Source number estimation and separation algorithms of underdetermined blind separation

被引：0
|
作者：
ZuYuan Yang
BeiHai Tan
GuoXu Zhou
JinLong Zhang
机构：
[1] South China University of Technology,School of Electronics & Information Engineering
来源：
Science in China Series F: Information Sciences | 2008年 / 51卷
关键词：
sparse representation; blind source separation; underdetermined mixing model; fuzzy clustering; mixing matrix;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Recently, sparse component analysis (SCA) has become a hot spot in BSS research. Instead of independent component analysis (ICA), SCA can be used to solve underdetermined mixture efficiently. Two-step approach (TSA) is one of the typical methods to solve SCA based BSS problems. It estimates the mixing matrix before the separation of the sources. K-means clustering is often used to estimate the mixing matrix. It relies on the prior knowledge of the source number strongly. However, the estimation of the source number is an obstacle. In this paper, a fuzzy clustering method is proposed to estimate the source number and mixing matrix simultaneously. After that, the sources are recovered by the shortest path method (SPM). Simulations show the availability and robustness of the proposed method.
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