通信辐射源个体识别中基于l2正则化的最大相关熵算法*

doi:10.16451/j.cnki.issn1003-6059.201606006

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Abstract To measure the similarity between the fine features of communication transmitters, an algorithm of individual communication transmitter identification using maximum correntropy based on l₂-regularization is put forward. Firstly, the square integral bispectra of the communication transmitter signal is extracted to characterize the individual differences of communication transmitters, and then optimization model using maximum correntropy criterion based on l₂-regularization is constructed. Next, the half-quadratic technique is used to transform the nonlinear optimization problem into a weighted linear least squares problem. Finally, the sparse coefficient can be computed by active set algorithm, and then the classifier is constructed by mining the discriminative information of coefficient for the communication transmitters identification. The feasibility and the effectiveness of the proposed algorithm are verified on the real datasets collected from the FM radios with same manufacturer and model.

Key words： Individual Communication Transmitter Identification Correntropy Sparse Representation Half-Quadratic Optimization l₂-Regularization

Received: 14 December 2015

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	TANG Zhe
	LEI Yingke

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TANG Zhe,LEI Yingke. Algorithm of Maximum Correntropy Based on l₂-Regularization in Individual Communication Transmitter Identification[J]. , 2016, 29(6): 527-533.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201606006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I6/527

[1] 许丹.辐射源指纹机理及识别方法研究.博士学位论文.长沙:国防科学技术大学, 2008.
(XU D. Research on Mechanism and Methodology of Specific Emitter Identification. Ph. D Dissertation. Changsha, China: National University of Defense Technology, 2008.)
[2] TEKBAS  H, RETEN O, SERINKEN N. Improvement of Transmitter Identification System for Low SNR Transients. Electronics Letters, 2004, 40(3): 182-183.
[3] SERINKEN N, RETEN O. Generalised Dimension Characterisa-tion of Radio Transmitter Turn-On Transients. Electronics Letters, 2000, 36(12): 1064-1066.
[4] GERDES R M, DANIELS T E, MINA M, et al. Device Identification via Analog Signal Fingerprinting: A Matched Filter Approach [EB/OL]. [2015-11-03]. http://www.internetsociety.org/sites/default/files/device_id_final.pdf.
[5] 蔡忠伟,李建东.基于双谱的通信辐射源个体识别.通信学报, 2007, 28(2): 75-79.
(CAI Z W, LI J D. Study of Transmitter Individual Identification Based on Bispectra. 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.电子学报, 2009, 37(10): 2125-2129, 2153.
(ZHANG M, ZHONG Z F, WANG R B. Research on the Technique of Individual Communication Transmitter Identification. Acta Electronica Sinica, 2009, 37(10): 2125-2129, 2153.)
[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.信号处理, 2012, 28(9): 1335-1340.
(GU C H, WANG L W. Individual Frequency Hopping Radio Identification Method Based on Instantaneous Envelope Characteristics. Signal Processing, 2012, 28(9): 1335-1340.)
[8] XU S H, HUANG B X, HUANG Y C, et al. Identification of Individual Radio Transmitters Based on Selected Surrounding-Line Integral Bispectra // Proc of the 9th International Conference on Advanced Communication Technology. Gangwon-Do, Repubic of Korea: IEEE, 2007: 1147-1150.
[9] GUI J, TAO D C, SUN Z N, et al. Group Sparse Multiview Patch Alignment Framework with View Consistency for Image Classification. IEEE Trans on Image Processing, 2014, 23(7): 3126-3137.
[10] GUI J, SUN Z N, JIA W, et al. Discriminant Sparse Neighborhood Preserving Embedding for Face Recognition. Pattern Recognition, 2012, 45(8): 2884-2893.
[11] GUI J, SUN Z N, TAN T N. Regularization Parameter Estimation for Spectral Regression Discriminant Analysis Based on Perturbation Theory // Proc of the 21st International Conference on Pattern Re-cognition. Tsukuba, Japan: IEEE, 2012: 401-404.
[12] WANG X B, WU Y Y, CARON B. Transmitter Identification Using Embedded Pseudo Random Sequences. IEEE Trans on Broadcasting, 2004, 50(3): 244-252.
[13] LIU W F, POKHAREL P P, PRINCIPE J C. Correntropy: Properties and Applications in Non-gaussian Signal Processing. IEEE Trans on Signal Processing, 2007, 55(11): 5286-5298.
[14] POKHAREL P P, LIU W F, PRINCIPE J C. A Low Complexity Robust Detector in Impulsive Noise. Signal Processing, 2009, 89(10): 1902-1909.
[15] LIU W F, POKHAREL P P, PRINCIPE J C. Correntropy: A Localized Similarity Measure // Proc of the IEEE International Joint Conference on Neural Network Proceedings. Vancouver, Canada: IEEE, 2006: 4919-4924.
[16] HE R, ZHENG W S, HU B G. Maximum Correntropy Criterion for Robust Face Recognition. IEEE Trans on Pattern Analysis and Machine Intelligence, 2010, 33(8): 1561-1576.
[17] WRIGHT J, YANG A Y, GANESH A, et al. Robust Face Recognition via Sparse Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2008, 31(2): 210-227.
[18] BOYD S, VAN DENBERGHE L. Convex Optimization. Cambridge, UK: Cambridge University Press, 2004.
[19] WRIGHT S J. On Reduced Convex QP Formulations of Monotone LCPs. Mathematical Programming, 2001, 90(3): 459-473.
[20] LEE H, BATTLE A, RAINA R, et al. Efficient Sparse Coding Algorithms [EB/OL]. [2015-11-10]. http://robotics.stanford.edu/~ang/papers/nips06-sparsecoding.pdf.