东南大学学报（自然科学版）2011，Vol.41Issue(6)：1127-1131,5.DOI:10.3969/j.issn.1001-0505.2011.06.002
基于CS与K-SVD的欠定盲源分离稀疏分量分析
Sparse presentation of underdetermined blind source separation based on compressed sensing and K-SVD
摘要
Abstract
To improve the precision of blind source separation, a method based on the compressed sensing (CS) and K-means singular value decomposition (K-SVD) is proposed. First, the equivalence between the problem of estimating the source in underdetermined blind source separation and the compressed sensing is analyzed and the framework of compressed sensing is built. Then K-SVD is used to train sparse dictionary self-adaptive under the framework. Finally the sparse component is computed using classic basis pursuit algorithm. Through lots of experiments the algorithm is proved to be a better algorithm, which inherits the advantages of sparse presentation ability and can significantly improve the precision of blind source separation. Different from traditional two steps methods , the algorithm proposed gets sparse presentation of signal taking a new way that combine CS and K-SVD, it shows that sparse presentation influences the result of blind resource separation directly.关键词
欠定盲源分离/稀疏表示/压缩感知Key words
underdetermined bund source separation/sparse representation/compressed sensing分类
信息技术与安全科学引用本文复制引用
余丰,奚吉,赵力,邹采荣..基于CS与K-SVD的欠定盲源分离稀疏分量分析[J].东南大学学报（自然科学版）,2011,41(6):1127-1131,5.基金项目
国家自然科学基金资助项目(60872073,60975017,51075068)、广东省自然科学基金资助项目(10252800001000001)、东南大学水声信号处理教育部重点实验室开放研究基金资助项目(UASP1003). （60872073,60975017,51075068）
