Blind Separation and Extraction of Binary Source

• LI Yuanqing

  Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute

• CICHOCKI Andrzej

  Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute

• ZHANG Liqing

  Department of Computer Science and Engineering, Shanghai Jiaotong University

抄録

This paper presents novel techniques for blind separation and blind extraction of instantaneously mixed binary sources, which are suitable for the case with less sensors than sources. First, a solvability analysis is presented for a general case. Necessary and sufficient conditions for recovcrability of all or some part of sources are derived. A new deterministic blind separation algorithm is then proposed to estimate the mixing matrix and separate all sources efficiently in the noise-free or low noise level case. Next, using the Maximum Likelihood (ML) approach for robust, estimation of centers of clusters, we have extended the algorithm for high additive noise case. Moreover, a new sequential blind extraction algorithm has been developed, which enables us not only to extract the potentially separable sources but also estimate their number. The sources can be extracted in a specific order according to their dominance (strength) in the mixtures. At last, simulation results are presented to illustrate the validity and high performance of the algorithms.

収録刊行物

• IEICE transactions on fundamentals of electronics, communications and computer sciences

  IEICE transactions on fundamentals of electronics, communications and computer sciences 86 (3), 580-589, 2003-03-01

  一般社団法人電子情報通信学会

キーワード

• blind separation
• blind extraction
• solvability conditions
• cluster
• noise

詳細情報

• CRID: 1572824502324545536
• NII論文ID: 110003212631
• NII書誌ID: AA10826239
• ISSN: 09168508
• 本文言語コード: en
• データソース種別:
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