基于最大相关熵的通信辐射源个体识别方法

doi:10.11959/j.issn.1000-436x.2016283

Abstract

Abstract:

To measure the similarity between the fine features of communication transmitters, a method of individual communication transmitter identification based on maximum correntropy was put forward. Firstly, the square integral bispectra was extracted to characterise the communication transmitters, and then optimization function based on maximum correntropy criterion was constructed. Secondly, the half-quadratic technique transformed the nonlinear op-timization problem to a weighted linear least squares problem. Finally, for identifying the communication transmitters, the sparse coefficient computed by active set algorithm was utilized to construct the classifier. Using the FM radios with same manufacturer and model, the actually collected data sets verified the feasibility and effectiveness of the method.

Key words: individual communication transmitter identification, maximum correntropy, sparse representation, half-quadratic, linear complementary problem, active set algorithm

Zhe TANG,Ying-ke LEI. Method of individual communication transmitter identification based on maximum correntropy[J]. Journal on Communications, 2016, 37(12): 171-175.

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[1]	DIETRICH J . A wavelet analysis of transients in phase-locked loops[D]. Winnipeg, Canada, University of Manitoba, 1994.
[2]	TEKBAS ? H , üRETEN O , SERINKEN N . Improvement of trans-mitter identification system for low SNR transients[J]. Electronics Letters, 2004,40(3):182-183.
[3]	SERINKEN N , üRETEN O . Generalised dimension characterisation of radio transmitter turn-on transients[J]. Electronics Letters, 2000,36(12):1064-1066.
[4]	GERDES R , DANIELS T , MINA M , et al. Device identification via analog signal fingerprinting: a matched filter approach[C]// 13th Annual Network and Distributed System Security Symposium. 2006.
[5]	蔡忠伟，李建东，．基于双谱的通信辐射源个体识别[J]．通信学报， 2007,28(2):75-79. CAI Z W , LI J D . Study of transmitter individual identification based on bispectra[J]. Journal on Communications, 2007,28(2):75-79.
[6]	张旻，钟子发，王若冰，．通信电台个体识别技术研究[J]．电子学报， 2009,37(10):2125-2129. ZHANG M , ZHONG Z F , WANG R B . Research on the technique of individual communication transmitter identification[J]. Acta Elec-tronica Sinica, 2009,37(10):2125-2129.
[7]	顾晨辉，王伦文．基于瞬时包络特征的跳频电台个体识别方法[J]．信号处理， 2012,28(9):1335-1340. GU C H , WANG L W . Individual frequency hopping radio identifica-tion method based on instantaneous envelope characteristics[J]. Signal Processing, 2012,28(9):1335-1340.
[8]	XU S H , HUANG B X , XU Z G . Identification of individual radio transmitters based on selected surrounding-line integral bispec-tra[C]// The 9th International Conference on Advanced Communication Technology. Gangwon-Do, South Korea, 2007:1147-1150.
[9]	WANG X B , WU Y Y , CARON B . Transmitter identification using embedded spread spectrum sequences[C]// 2003 International Confer-ence on Communication Technology. Beijing, China, 2003:1517-1523.
[10]	WANG X B , WU Y Y , CARON B . Transmitter identification using embedded pseudo random sequences[J]. IEEE Transactions on Broad-casting, 2004,50(3):244-252.
[11]	LIU W , POKHAREL P P , PRINCIPE J C . Correntropy: properties and applications in non-Gaussian signal processing[J]. IEEE Transactions on Signal Processing, 2007,55(11):5286-5298.
[12]	POKHAREL P P , LIU W F , PRINCIPLE J C . A low complexity robust detector in impulsive noise[J]. Signal Processing, 2009,89(10):1902-1909.
[13]	LIU W F , POKHAREL P P , PRINCIPLE J C . Correntropy: a localized similarity measure[C]// 2006 International Joint Conference on Neural Networks. Vancouver, Canada, 2006:4919-4924.
[14]	HE R , ZHENG W S , HU B G . Maximum correntropy criterion for robust face recognition[C]// IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011,33(8):1561-1576.
[15]	WRIGHT J , YANG A Y , GANESH A , et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009,31(2):210-217.
[16]	BOYD S , VANDENBERGHE L . Convex optimization[M]. New York: Cambridge University Press, 2004.
[17]	WRIGHT S J . On reduced convex QP formulations of monotone LCPs[J]. Mathematical Programming, 2001,90(3):459-473.
[18]	LEE H , BATTLE A , RAINA R , et al. Efficient sparse coding algo-rithms[C]// The Twentieth Annual Conference on Neural Information Processing Systems. Vancouver, Canada, 2006:4-7.

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训练样本个数	NNC	SVM	CESRC	MCERC
20	49.94%	46.72%	54.81%	56.91%
40	59.18%	50.54%	66.36%	68.21%
60	65.33%	54.38%	72.17%	74.79%
80	67.00%	55.00%	76.00%	76.95%
100	68.31%	57.69%	76.75%	79.56%
120	71.67%	57.17%	81.25%	81.83%
140	70.13%	56.00%	78.88%	80.50%
160	73.25%	58.25%	81.50%	83.25%

Method of individual communication transmitter identification based on maximum correntropy

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