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Sequential blind source separation based exclusively on second-order statistics developed for a class of periodic signals

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journal contribution
posted on 04.12.2009, 15:11 by Maria G. Jafari, Wenwu Wang, Jonathon Chambers, Tetsuya Hoya, Andrzej Cichocki
A sequential algorithm for the blind separation of a class of periodic source signals is introduced in this paper. The algorithm is based only on second-order statistical information and exploits the assumption that the source signals have distinct periods. Separation is performed by sequentially converging to a solution which in effect diagonalizes the output covariance matrix constructed at a lag corresponding to the fundamental period of the source we select, the one with the smallest period. Simulation results for synthetic signals and real electrocardiogram recordings show that the proposed algorithm has the ability to restore statistical independence, and its performance is comparable to that of the equivariant adaptive source separation (EASI) algorithm, a benchmark high-order statistics-based sequential algorithm with similar computational complexity. The proposed algorithm is also shown to mitigate the limitation that the EASI algorithm can separate at most one Gaussian distributed source. Furthermore, the steady-state performance of the proposed algorithm is compared with that of EASI and the block-based second-order blind identification (SOBI) method.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

JAFARI, M.G. ...et al, 2006. Sequential blind source separation based exclusively on second-order statistics developed for a class of periodic signals. IEEE Transactions on Signal Processing, 54 (3), pp. 1028-1040.

Publisher

© IEEE

Version

VoR (Version of Record)

Publication date

2006

Notes

This article was published in the journal IEEE Transactions on Signal Processing [© IEEE] and is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISSN

1053-587X

Language

en