Blind Identification of Underdetermined Mixtures Using Second-Order Cyclostationary Statistics

LU Fengbo; ZHANG Bisheng; HUANG Zhitao; JIANG Wenli

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(1): 31-35

LU Fengbo, ZHANG Bisheng, HUANG Zhitao, et al., “Blind Identification of Underdetermined Mixtures Using Second-Order Cyclostationary Statistics,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 31-35, 2013,

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LU Fengbo, ZHANG Bisheng, HUANG Zhitao, et al., “Blind Identification of Underdetermined Mixtures Using Second-Order Cyclostationary Statistics,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 31-35, 2013,

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Blind Identification of Underdetermined Mixtures Using Second-Order Cyclostationary Statistics

1.
Naval Academy of Armament, Beijing 100161, China;
2.
School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China

Received Date: 2010-12-01
Rev Recd Date: 2012-07-01
Publish Date: 2013-01-05

Abstract

Abstract

Aiming at the problem of blind identification of underdetermined mixtures, we propose a method of blind identification based on second-order cyclostationary statistics. Estimate non zero cycle frequencies of original signals by cycle auto-correlation function of mixtures first and then stack the cycle correlation matrices corresponding to different cycle frequencies and time lags in a three order tensor, final achieve the estimation of mixing matrix by canonical decomposition. Simulation results indicate that the proposed algorithm estimates the mixing matrix with higher accuracy compared to the conventional algorithms.

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References

P. Bofill and M. Zibulevsky, “Underdetermined blind source separationusing sparse representations”, Signal Processing, Vol.81,No.11, pp.2353-2362, 2001.

P. Georgiev and F. Theis, “Sparse component analysis and blindsource separation of underdetermined mixtures”, IEEE Transon Neural Networks, Vol.16, No.4, pp.992-996, 2005.

M. Aharon, M. Elad, A. Bruckstein, “K-SVD: An algorithm fordesigning overcomplete dictionaries for sparse representation”,IEEE Trans. on Signal Processing, Vol.54, No.11, pp.4311-4322, 2006.

Z.S. He and A. Cichocki, “K-hyperline clustering learning forsparse component analysis”, Signal Processing, Vol.89, No.6,pp.1011-1022, 2009.

F. Abrard and Y. Deville, “A time-frequency blind signal separationmethod applicable to underdetermined mixtures of dependentsources”, Signal Processing, Vol.85, No.6, pp.1389-1403, 2005.

M. Puigt and Y. Deville, “Time-frequency ratio-based blind separationmethods for attenuated and time-delayed sources”, MechanicalSystems and Signal Processing, Vol.19, No.6, pp.1348-1379, 2005.

Y.Q. Li, S.I. Amari and A. Cichocki, “Underdetermined blindsource separation based on sparse representation”, IEEE Trans.on Signal Processing, Vol.54, No.2, pp.423-437, 2006.

A. Aissa-El-Bey and N. Linh-Trung, “Underdetermined blindseparation of nondisjoint sources in the time-frequency domain”,IEEE Trans. on Signal Processing, Vol.55, No.3,pp.897-907, 2007.

S.G. Kim and Chang D. Yoo, “Underdetermined blind sourceseparation based on subspace representation”, IEEE Trans. onSignal Processing, Vol.57, No.7, pp.2604-2614, 2009.

M.S. Lewicki and T.J. Sejnowski, “Learning overcomplete representations”,Neural Computation, Vol.12, No.2, pp.337-365,2000.

Z.S. He, S.L. Xie and A. Cichocki, “Convolutive blind sourceseparation in frequency domain based on sparse representation”,IEEE Transactions on Audio, Speech and Language Processing,Vol.15, No.5, pp.1551-1563, 2007.

Z.S. He, S.L. Xie and A. Cichocki, “A note on Lewicki-Sejnowski gradient for learning overcomplete representation”,Neural Computation, Vol.20, No.3, pp.636-643, 2008.

P. Comon, “Blind identification and source separation in 2 × 3underdetermined mixtures”, IEEE Trans. on Signal Processing,Vol.52, No.1, pp.11-22, 2004.

A. Ferréol, L. Albera and P. Chevalier, “Fourth-order blind identificationof underdetermined mixtures of sources”, IEEE Trans.on Signal Processing, Vol.53, No.5, pp.1640-1653, 2005.

L.D. Lathauwer, J. Castaing and J.F. Cardoso, “Four-ordercumulant-based blind identification of underdetermined mixtures”,IEEE Trans. on Signal Processing, Vol.55, No.6,pp.2965-2973, 2007.

L.D. Lathauwer and J. Castaing, “Blind identification of underdeterminedmixtures by simultaneous matrix diagonalization”,IEEE Trans. on Signal Processing, Vol.56, No.3, pp.1096-1105,2008.

A. Karfoul, L. Albera and G. Birot, “Blind underdeterminedmixture identification by joint canonical decomposition of HOcumulants”, IEEE Trans. on Signal Processing, Vol.58, No.2,pp.638-649, 2010.

L.D. Lathauwer, “A link between the canonical decompositionin multilinear algebra and simultaneous matrix diagonalization”,SIAM J. Matrix Anal. Appl, Vol.28, No.3, pp.642-666,2006.

A. Stegeman and J.M.F. Ten Berge, “Kruskal’s condition foruniqueness in Candecomp/Parafac when ranks and k-ranks coincide”,Computational Statistics and Data Analysis, Vol.50,No.4, pp.210-220, 2006.

N.D. Sidiropoulos, G.B. Giannakis and R. Bro, “BlindPARAFAC receivers for DS-CDMA systems”, IEEE Trans. onSignal Processing, Vol.48, No.3, pp.810-823, 2000.

D. Nion and L. De Lathauwer, “Line search computation ofthe block factor model for blind multi-user access in wirelesscommunications”, Proceedings of SPAWC’06, Cannes, France,pp.2-5, 2006.

D. Nion and L. De Lathauwer, “An enhanced line search schemefor complex-valued tensor decompositions. Application in DSCDMA”,Signal Processing, Vol.88, No.7, pp.749-755, 2008.

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