Application of underdetermined blind source separation in ultra-wideband communication signals

被引：1

作者：

机构：

[1] College of Information and Communication Engineering, Harbin Engineering University

[2] EMC Laboratory, Beijing University of Aeronautics and Astronautics

来源：

Guo, H. (chinamengh823@126.com) | 1600年 / Beijing University of Posts and Telecommunications卷 / 20期

关键词：

amended subspace projection; hough-windowed; single source area detection; UBSS; UWB;

D O I：

10.1016/S1005-8885(13)60042-4

中图分类号：

学科分类号：

摘要：

Aiming to the estimation of source numbers, mixing matrix and separation of mixing signals under underdetermined case, the article puts forward a method of underdetermined blind source separation (UBSS) with an application in ultra-wideband (UWB) communication signals. The method is based on the sparse characteristic of UWB communication signals in the time domain. Firstly, finding the single source area by calculating the ratio of observed sampling points. Then an algorithm called hough-windowed method was introduced to estimate the number of sources and mixing matrix. Finally the separation of mixing signals using a method based on amended subspace projection. The simulation results indicate that the proposed method can separate UWB communication signals successfully, estimate the mixing matrix with higher accuracy and separate the mixing signals with higher gain compared with other conventional algorithms. At the same time, the method reflects the higher stability and the better noise immunity. © 2013 The Journal of China Universities of Posts and Telecommunications.

引用

页码：13 / 19

页数：6

共 18 条

[1]

Zhao C.H., Liu J., Li J., Et al., An effective method on blind speech separation in strong noisy environment, Proceedings of the 2005 IEEE International Workshop on VLSI Design Video Technology (IWVDVT'05), May 28-30, 2005, Suzhou, China, pp. 211-214, (2005)

[2]

Bartlett M.S., Movellan J.R., Sejnowski T.J., Face recognition by independent component analysis, IEEE Transactions on Neural Networks, 13, 6, pp. 1450-1464, (2002)

[3]

Cristescu R., Joutesensalo J., Ristaniemi T., Fading channel estimation by mutual information minimization for gaussian stochastic processes, Proceedings of the IEEE International Conference on Communications (ICC'00): Vol 1, Jun 18-22, 2000, New Orleans, LA, USA, pp. 56-59, (2000)

[4]

Sun L.S., Liu Y., Beadle P.J., Independent component analysis of EEG signals, Proceedings of the 2005 IEEE International Workshop on VLSI Design Video Technology (IWVDVT'05), May 28-30, 2005, Suzhou, China, pp. 219-222, (2005)

[5]

Liu C.J., Wechsler H., Independent component analysis of gabor features for face recognition, IEEE Transactions on Neural Networks, 14, 4, pp. 919-928, (2003)

[6]

Zhang J., Lin J.H., Improved blind source separation method based on independent component analysis and empirical mode decomposition, Proceedings of the 4th International Conference on Biomedical Engineering and Informatics (BMEI'11): Vol 4, Oct 15-17, 2011, Shanghai, China, pp. 2217-2219, (2011)

[7]

Reyhani N., Ylipaavalniemi J., Vigario R., Et al., Consistency and asymptotic normality of FastICA and bootstrap FastICA, Signal Processing, 92, 8, pp. 1767-1778, (2012)

[8]

Cardoso J.F., Souloumiac A., Blind beamforming for non-Gaussian signals, IEE Proceedings-F, 140, 6, pp. 362-370, (1993)

[9]

Xie S.L., He Z.S., Fu Y.L., A note on stone's conjecture of blind separation, Neural Computation, 117, 2, pp. 321-330, (2005)

[10]

Cemgil A.T., Fevotte C., Godsiil S.J., Variational and stochastic inference for Bayesian source separation, Digital Signal Processing, 17, 5, pp. 891-913, (2007)

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