基于半监督矩形网络的通信电台个体识别
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摘要
通信电台信号的小样本条件造成了电台个体识别准确性欠佳的问题,本文首次提出基于半监督矩形网络进行通信电台个体识别,克服小样本条件对电台个体识别效果的影响.首先提取电台信号的矩形积分双谱特征,人为注入噪声构成污染样本,在半监督矩形网络编码器中有监督训练,其训练结果通过网络径向连接传给解码器,解码器再无监督学习,重构未污染的原始样本,从网络顶层提取电台个体特征,输入softmax分类器实现分类识别.在实际采集的电台数据集上的实验结果说明,本算法在小样本条件下相比现有算法能更准确识别同型号的电台个体.
Small sample condition of communication radio signal caused poor individual recognition on radios. To solve this problem,a method about communication radio individual recognition based on semi-supervised rectangular network was proposed innovatively. Firstly, the square integral bispectrum feature was extracted from radio signal and then was corrupted by Gaussian noise. The corrupted sample was passed to the encoder of semi-supervised rectangular network for supervised training. The trained parameterization was then mirrored to decoder through the lateral connection across the model.And the output was forced by decoder through unsupervised learning to be close to the clean input. Then the essential feature extracted was referred as the individual feature of radio signals. Individual recognition was finally accomplished by a softmax classifier. The experiment results on several radio datasets collected in actual environment indicated that the method had superior performance on identifying radio individuals with the same types under small sample condition.
Small sample condition of communication radio signal caused poor individual recognition on radios. To solve this problem,a method about communication radio individual recognition based on semi-supervised rectangular network was proposed innovatively. Firstly, the square integral bispectrum feature was extracted from radio signal and then was corrupted by Gaussian noise. The corrupted sample was passed to the encoder of semi-supervised rectangular network for supervised training. The trained parameterization was then mirrored to decoder through the lateral connection across the model.And the output was forced by decoder through unsupervised learning to be close to the clean input. Then the essential feature extracted was referred as the individual feature of radio signals. Individual recognition was finally accomplished by a softmax classifier. The experiment results on several radio datasets collected in actual environment indicated that the method had superior performance on identifying radio individuals with the same types under small sample condition.
引文
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[26]陶旺林,卢选民,刘李娟.基于局部围线双谱的通信辐射源个体识别[J].计算机工程与应用,2013,49(1):131-133.TAO Wang-lin,LU Xuan-min,LIU Li-juan. Transmitter individual identification based on local surrounding-line integral bispectrum[J]. Computer Engineering and Applications,2013,49(1):131-133.(in Chinese)
[2]许丹,柳征,姜文利,周一宇.窄带信号中的放大器“指纹”特征提取:原理分析及FM广播实测实验[J].电子学报,2008,36(5):917-932.XU Dan,LIU Zheng,JIANG Wen-li,ZHOU Yi-yu. Extraction of amplifier fingerprints from narrow band signal:principle analysis and FM broadcast experiment[J]. Acta Electronica Sinica,2008,36(5):917-932.(in Chinese)
[3]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 in,ZHONG Zi-fa,WANG Ruo-bin. Research on the technique of individual communication transmitter identi-fication[J]. Acta Electronica Sinica,2009,37(10):2125-2129.(in Chinese)
[4] HALL J,BARBEAU M,KRANAKIS E. Radio frequency fingerprinting for intrusion detection in w ireless netw orks[J]. IEEE Transactions on Defend-able and Secure Computing,2005(12):1-35.
[5] BERTONCINI C,RUDD K,NOUSAIN B,et al. Wavelet fingerprinting of radio-frequency identification(RFID)tags[J]. IEEE Transactions on Industrial Electronics,2012,59(12):4843-4850.
[6]XU S H. Radio transmitter classification using a new method of stray features analysis combined w ith PCA[A]. M ilitary Communication Conference[C]. Orlando,FL,USA:IEEE,2007. 1-5.
[7]唐智灵.通信辐射源非线性个体识别方法研究[D].西安:西安电子科技大学,2013.TANG Zhi-ling. A Study of Nonlinear M ethod for Specific Communications Emitter Identification[D]. Xian:Xidian University,2013.(in Chinese)
[8]梁江海.一种基于经验模态分解的通信辐射源个体识别方法[J].中国电子科学研究院学报,2013,8(4):393-397.LIANG Jiang-hai. A method based on empirical mode decomposition for identifying transmitter individuals[J].Journal of CAEIT,2013,8(4):393-397.(in Chinese)
[9]韩洁,张涛,王欢欢,等.基于3D-Hibert能量谱和多尺度分形特征的通信辐射源个体识别[J].通信学报,2017,38(4):99-109.HAN Jie,ZHANG Tao,WANG Huan-huan,et al. Communication emitter individual identification based on 3D-Hibbert energy spectrum and multi-scale fractal features[J].Journal on Communications,2017,38(4):99-109.(in Chinese)
[10]汪勇,段田东,刘瑞东,等.短时频率稳定度特征分析的FSK信号个体识别方法[J].太赫兹科学与电子信息学报,2013,(6):880-885.WANG yong,DUAN Tian-dong,LIU Rui-dong,et al. Individual identification of FSK signals based on stability of transient carrier frequency[J]. Journal of Terahertz Science and Electronic Information Technology,2013,(6):880-885.(in Chinese)
[11]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 Chen-hui,WANG Lun-w en. Individual frequency hopping radio identification method based on instantaneous envelope characteristics[J]. Signal Processing,2012,28(9):1335-1340.(in Chinese)
[12]陈志伟,徐志军,王金明.一种基于循环谱切片的通信辐射源识别方法[J].数据采集与处理,2013,28(3):284-288.CHEN Zhi-w ei,XU Zhi-jun,WANG Jin-ming,et al. Emitter identification method based on cyclic spectrum density slice[J]. Journal of Data Acquisition and Processing,2013,28(3):284-288.(in Chinese)
[13]唐哲,雷迎科.基于最大相关熵的通信辐射源个体识别方法[J].通信学报,2016,37(12):171-175.TANG Zhe,LEI Ying-ke. M ethod of individual communication transmitter identification based on maximum correntropy[J]. Journal on Communications,2016,37(12):171-175.(in Chinese)
[14]孙志军,薛磊,徐阳明.基于深度学习的边际Fisher分析特征提取算法[J].电子与信息学报,2013,35(4):805-811.SUN Zhi-jun,XUE Lei,XU Yang-ming. M arginal fisher feature extraction algorithm based on deep learning[J].Journal of Electronics and Information Technology,2013,35(4):805-811.(in Chinese)
[15]WU Z,TAKAKI S,YAMAGASHI J. Deep Denoising Auto-Encoder for Statistical Speech Synthesis[OL]. https://arxiv. org/abs/1511. 06430,2015.
[16]赵飞翔,刘永祥,霍凯.基于栈式降噪稀疏自动编码器的雷达目标识别方法[J].雷达学报,2017,6(2):149-156.ZHAO Fei-xiang,LIU Yong-xiang,HUO Kai. Radar target recognition based on stacked denoising sparse autoencoder[J]. Journal of Radars,2017,6(2):149-156.(in Chinese)
[17]VALPOLA H. Chapter 8-From Neural PCA to Deep Unsupervised Learning[A]. Advances in Independent Component Analysis and Learning M achines[M]. USA:Elsevier Ltd,2015.
[18]VINCENT P,LAROCHELLE H,LAJOIE I,et al. Stacked denoising autoencoders:Learning useful representations in a deep netw ork w ith a local denoising criterion[J]. Journal of M achine Learning Research,2010,11(12):3371-3408.
[19]IOFFE S,SZEGEDY C. Batch Normalization:Accelerating Deep Network Training by Reducing Internal Covariate Shift[OL]. https://arxiv. org/abs/1502. 03167,2015.
[20] VINCENT P,LAROCHELLE H,BENGIO Y,et al. Extracting and composing robust features w ith denoising auto-encoders[A]. International Conference on M achine Learning[C]. Helsinki,Finland:ACM,2008. 1096-1103.
[21] HAGER W,ZHANG H. The limited memory conjugate gradient method[J]. Siam Journal on Optimization,2013,23(4):2150-2168.
[22]PEZESHKI M,FAN L,BRAKEL P,et al. Deconstructing the Ladder Netw ork Architecture[OL]. https://arxiv.org/abs/1511. 06430,2015.
[23]ZHANG X,CHEUNG W K,LI C H. Learning latent variable models from distributed and abstracted data[J]. Information Sciences,2011,181(4):2964-2988.
[24]SCHMIDHUBER J. Deep learning in neural networks:An overview[J]. Neural Netw orks the Official Journal of the International Neural Netw ork Society,2014,61:85-117.
[25]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 Zhong-w ei,LI Jian-dong. Study of transmitter individual identification based on bispectra[J]. Journal on Communications,2007,28(2):75-79.(in Chinese)
[26]陶旺林,卢选民,刘李娟.基于局部围线双谱的通信辐射源个体识别[J].计算机工程与应用,2013,49(1):131-133.TAO Wang-lin,LU Xuan-min,LIU Li-juan. Transmitter individual identification based on local surrounding-line integral bispectrum[J]. Computer Engineering and Applications,2013,49(1):131-133.(in Chinese)