半监督条件下的CRC跳频电台指纹特征识别
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摘要
针对跳频电台指纹特征差异细微、对噪声影响敏感,同时非合作条件下跳频信号的识别训练标签数据不足问题,提出了一种基于合作表征分类器(collaborative representation classifier,CRC)的半监督条件下跳频电台指纹特征识别算法。以跳频电台开机瞬态信号的包络特性作为电台个体的指纹特征,利用对噪声"不敏感"的高阶累积量估计来抑制噪声;通过构造半监督条件下的CRC实现对未标定训练数据的有效利用。实验表明,与传统有监督训练相比,该方法在抑制噪声的同时,能够充分利用未标定训练数据特征,对目标特征具有更高的识别率。
The fingerprints difference between the individual transmitters is so subtle and can be seriously affected by noise,and the labeled training data samples are difficult to obtain especially in non-collaborative conditions.To solve these problems,we propose a transmitter fingerprint feature recognition method based on semi-supervised collaborative representation classifier(CRC).The envelope properties of the boot signal are adopted as the fingerprint features of individual transmitters.In order to reduce the effect of ambient noise,a noise suppression method is given based on higher order cumulant.And finally the semi-supervised CRC is constructed to classify the feature results.Experiments demonstrate that our method could suppress the noise effectively,and have higher recognition rate by making effective use of the data features of unlabeled samples.
The fingerprints difference between the individual transmitters is so subtle and can be seriously affected by noise,and the labeled training data samples are difficult to obtain especially in non-collaborative conditions.To solve these problems,we propose a transmitter fingerprint feature recognition method based on semi-supervised collaborative representation classifier(CRC).The envelope properties of the boot signal are adopted as the fingerprint features of individual transmitters.In order to reduce the effect of ambient noise,a noise suppression method is given based on higher order cumulant.And finally the semi-supervised CRC is constructed to classify the feature results.Experiments demonstrate that our method could suppress the noise effectively,and have higher recognition rate by making effective use of the data features of unlabeled samples.
引文
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[23]HU G,GONG Y,CHEN Y,et al.Semi-supervised radio transmitter classification based on elastic sparsity regularized SVM[J].Journal of Electronics,2012,29(6):501-508.
[24]MLLER K R,MIKA S,RTSCH G,et al.An introduction to kernel-based learning algorithms[J].IEEE Trans.on Neural Networks,2001,12(2):181.
[25]BOSER B E.A training algorithm for optimal margin classifiers[C]∥Proc.of the Workshop on Computational Learning Theory,1992:144-152.
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[30]TUGNAIT J K.Detection of non-Gaussian signals using integrated polyspectrum[J].IEEE Trans.on Signal Processing,1994,42(11):3137-3149.
[31]LIAO X,BAO Z.Circularly integrated bispectra:novel shift invariant features for high resolution radar target recognition[J].Electronics Letters,1998,34(19):1879-1880.
[32]CHAN Y T,GADBOIS L G.Identification of the modulation type of a signal[J].Signal Processing,1985,16(2):149-154.
[2]ERIC M,DUKIC M L,OBRADOVIC M.Frequency hopping signal separation by spatio-frequency analysis based on MUSIC method[J].Spread Spectrum Techniques and Applications,2000,66(1):78-82.
[3]王斌,陈秋华,王翠柏.基于聚类的跳频信号分选[J].北京邮电大学学报,2009,32(2):80-84.WANG B,CHEN Q H,WANG C B.Identification of frequency hopping signals based on clustering[J].Journal of Beijing University of Posts and Telecommunications,2009,32(2):80-84.
[4]李文龙,梁涛,许金勇.跳频通信中的射频指纹识别[D].南京:解放军理工大学,2009.LI W L,LIANG T,XU J Y.Radio frequency fingerprinting in frequency hopping communication[D].Nanjing:PLA University of Science&Technology,2009.
[5]骆振兴,赵知劲.基于功放瞬态响应的跳频电台分选方法[J].杭州电子科技大学学报,2009,29(1):29-32.LUO Z X,ZHAO Z J.Individual HF transmitter identification based on transient response of power amplifier[J].Journal of Hangzhou Dianzi University,2009,29(1):29-32.
[6]高勇.时频分析与盲信号处理[M].北京:国防工业出版社,2017.GAO Y.Time-frequency analysis and blind signal processing[M].Beijing:National Defense Industry Press,2017.
[7]FAN H,FAN H,MENG Q,et al.Feature detection based on filter banks and higher order cumulants[C]∥Proc.of the IEEE International Conference on Information Acquisition,2006:562-566.
[8]YANG S,FENG Z,REN Y,et al.Semi-supervised classification via kernel low-rank representation graph[J].Knowledge-Based Systems,2014,69(1):150-158.
[9]GAO Y,MA J,ZHAO M,et al.Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples[J].IEEE Trans.on Image Processing,2017,26(5):2545-2560.
[10]SONG T,CAI J,ZHANG T,et al.Semi-supervised manifoldembedded hashing with joint feature representation and classifier learning[J].Pattern Recognition,2017,68(C):99-110.
[11]CANDS E J,ROMBERG J K,TAO T.Stable signal recovery from incomplete and inaccurate measurements[J].Common Pure&Applied Math,2006,59(8):1207-1223.
[12]CANDES E J,TAO T.Near-optimal signal recovery from random projections:universal encoding strategies?[J].IEEE Trans.on Information Theory,2006,52(12):5406-5425.
[13]DONOHO D.For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution[J].Common Pure&Applied Math,2006,59(6):797-829.
[14]FENG Q,YUAN C,PAN J S,et al.Superimposed sparse parameter classifiers for face recognition[J].IEEE Trans.on Cybernetics,2017,47(2):378-390.
[15]TANG Z,LEI Y K.Radio transmitter identification based on collaborative representation[J].Wireless Personal Communications,2017,96(1):1377-1391.
[16]DENG W,HU J,GUO J.Extended SRC:under-sampled face recognition via intraclass variant dictionary[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,2012,34(9):1864-1870.
[17]WANG L,CHEN S.Joint representation classification for collective face recognition[J].Pattern Recognition,2017,63(5):182-192.
[18]ZHANG L,YANG M.Sparse representation or collaborative representation:which helps face recognition?[C]∥Proc.of the IEEE International Conference on Computer Vision,2012:471-478.
[19]SATTAR F,SALOMONSSON G.On detection using filter banks and higher order statistics[J].IEEE Trans.on Aerospace&Electronic Systems,2000,36(4):1179-1189.
[20]范虹.非平稳信号特征提取方法及应用[M].北京:科学出版社,2013.FAN H.Non-stationary signal feature extraction and application[M].Beijing:Science Press,2013.
[21]BELKIN M,NIYOGI P,SINDHWANI V.Manifold regularization:ageometric framework for learning from labeled and unlabeled examples[J].Journal of Machine Learning Research,2006,7(1):2399-2434.
[22]FAN M,GU N,QIAO H,et al.Sparse regularization for semi-supervised classification[J].Pattern Recognition,2011,44(8):1777-1784.
[23]HU G,GONG Y,CHEN Y,et al.Semi-supervised radio transmitter classification based on elastic sparsity regularized SVM[J].Journal of Electronics,2012,29(6):501-508.
[24]MLLER K R,MIKA S,RTSCH G,et al.An introduction to kernel-based learning algorithms[J].IEEE Trans.on Neural Networks,2001,12(2):181.
[25]BOSER B E.A training algorithm for optimal margin classifiers[C]∥Proc.of the Workshop on Computational Learning Theory,1992:144-152.
[26]MERCER J.Functions of positive and negative type,and their connection with the theory of integral equations[J].Philosophical Transactions of the Royal Society of London,1909,209:415-446.
[27]VAPNIK V.The nature of statistical learning theory[M].New York:Springer,1995.
[28]CHERKASSKY V,MULIER F.Learning from data:concept,theory and method[M].New York:Wiley,1997.
[29]CHANDRAN V,ELGAR S L.Pattern recognition using invariants defined from higher order spectra to one-dimensional inputs[J].IEEE Trans.on Signal Processing,1993,41(1):205-212.
[30]TUGNAIT J K.Detection of non-Gaussian signals using integrated polyspectrum[J].IEEE Trans.on Signal Processing,1994,42(11):3137-3149.
[31]LIAO X,BAO Z.Circularly integrated bispectra:novel shift invariant features for high resolution radar target recognition[J].Electronics Letters,1998,34(19):1879-1880.
[32]CHAN Y T,GADBOIS L G.Identification of the modulation type of a signal[J].Signal Processing,1985,16(2):149-154.