Blind separation and extraction of binary sources

Yuanqing Li, Andrzej Cichocki, Liqing Zhang

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


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 recoverability 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 of 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.

Original languageEnglish
Pages (from-to)580-589
Number of pages10
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Issue number3
Publication statusPublished - Mar 2003
Externally publishedYes


  • Blind extraction
  • Blind separation
  • Cluster
  • Noise
  • Solvability conditions


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