欠定盲信号分离的高阶张量分析方法研究

项目名称： 欠定盲信号分离的高阶张量分析方法研究

项目编号： No.61273192

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 谢胜利

作者单位： 广东工业大学

项目金额： 83万元

中文摘要： 盲信号处理是现代信号处理中具有重要理论价值和巨大潜在应用的新学科分支，近年来一直是国内外关注的热点，而欠定混叠的盲信号分离是其中的难点问题。本课题针对信号向量的高阶统计量恰好为具有超对称结构的高阶张量的特性，探讨利用高阶张量分析方法挖掘隐藏在信号中的高阶频谱，致力于解决欠定盲信号分离研究中的几个关键问题： 1. 欠定盲信号分离中源信号数目的盲估计问题（或稀疏信号盲分离中信道聚类分析的类数目估计问题）； 2. 欠定盲信号分离中传输信道的盲辨识问题（或稀疏信号盲分离中信道聚类分析的类中心估计问题）； 3. 相应于高阶张量分析方法的欠定盲信号分离算法及收敛性分析问题。 并尝试性的探讨盲信号分离的具体实际应用问题（因为目前这方面还没有真正的实际应用案例）。

中文关键词： 欠定盲分离；高阶张量；平行因子分析；；

英文摘要： Blind Source Separation (BSS), which is very important both in theory and practice, has found many potential applications in signal processing filed. Due to this, it has received a lot of attention around the world recently. However, the underdetermined BSS problem is very challenging and remains unsolved largely nowadays, where the higher-order tensor analysis is potentially a powerful tool to solve this problem. Exploiting the multi-linearity between higher-order cumulants of the mixtures and mixing matrix, this project focuses on the higher-order tensor decomposition methods for underdetermined BSS and solving several challenging problems as follows: 1) Identifying the number of sources in underdetermined BSS, which is equivalent to estimate the number of columns of mixing matrix in blind separation of sparse sources; 2) Blind identifying the mixing channels in underdetermined BSS, or estimating the cluster centroids if we solve the blind separation of sparse sources by clustering methods; 3) Developing more efficient higher-order tensor decomposition algorithms for underdetermined BSS problem and building rigorous convergence theory for them. In addition, currently we expect to find more applications and more successful applications for BSS in spite of its advances. In this project, we would like to investig

英文关键词： Underdetermined blind source separation；higher-ord tensor；Paeallel Factor analysis；；