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  1. (1. 西安电子科技大学 雷达信号处理重点实验室, 陕西 西安 710071;
    2. 清华大学 自动化系 智能技术与系统国家重点实验室, 北京 100084)

  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2004-06-20 发布日期:2004-06-20

Blind source separation: classification and frontiers

LI Xiao-jun1;ZHU Xiao-long2;ZHANG Xian-da2


  1. (1. Key Lab. of Radar Signal Processing, Xidian Univ., Xi'an 710071, China;
    2. State Key Lab. of Intelligent Technology and Systems, Dept. of Automation, Tsinghua Univ., Beijing 100084, China)
  • Received:1900-01-01 Revised:1900-01-01 Online:2004-06-20 Published:2004-06-20

摘要: 通过对盲信号分离最新发展的研究,对现有的盲信号分离研究理论进行了总结分类,提取了在盲信号分离中主要使用的优化准则,通过介绍对比函数理论和局部稳定性理论,以及算法的性能指标,给出了盲信号分离的研究框架,并对盲信号分离的进一步研究方向进行了有关展望.

关键词: 盲信号分离, 独立分量分析, 优化准则, 性能指标

Abstract: By researching into the new development of BSS, this paper is dedicated to the classification of the BSS algorithms and the researching frame of BSS, together with getting the crucial optimal criteria of BSS, and introducing the contrast function theory, the local stability condition and the performance indices. Finally, the frontiers in the research of BSS are also presented.

Key words: blind source separation(BSS), independent component analysis(ICA), optimized criteria, performance index


  • TN911.23