《辐射源信号识别探究的国内外文献综述》4500字

辐射源信号识别研究的国内外文献综述1辐射源个体识别研究现状特定辐射源识别技术（SpecificEmitterIdentification，SEI）是由美国军队率先进行研究并确立相关概念，我国在该领域起步较晚，从20世纪80年代开始研究至今已有四十余年的发展与改进，总体框架已然确立，如REF_Ref50928055\h图11所示，但技术细节仍在不断发展。最开始雷达辐射源识别技术比较接近于传统信号处理，如文献ADDINEN.CITE<EndNote><Cite><Author>Cooper</Author><Year>2009</Year><RecNum>181</RecNum><DisplayText><styleface="superscript">[1]</style></DisplayText><record><rec-number>181</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1535596792">181</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Cooper,D.C.</author></authors></contributors><titles><title>ElectronicIntelligence:theAnalysisofRadarSignals</title><secondary-title>Electronics&Power</secondary-title></titles><periodical><full-title>Electronics&Power</full-title></periodical><pages>242</pages><volume>30</volume><number>3</number><dates><year>2009</year></dates><urls></urls></record></Cite></EndNote>[\o"Cooper,2009#181"1]对辐射源信号常规特征进行分析研究，较少了电子对抗侦察系统的组成；文献ADDINEN.CITE<EndNote><Cite><Author>Hassan</Author><Year>2005</Year><RecNum>179</RecNum><DisplayText><styleface="superscript">[2]</style></DisplayText><record><rec-number>179</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1535592861">179</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>S.A.Hassan</author><author>A.I.Bhatti</author><author>A.Latif</author></authors></contributors><titles><title>Emitterrecognitionusingfuzzyinferencesystem</title><secondary-title>ProceedingsoftheIEEESymposiumonEmergingTechnologies,2005.</secondary-title><alt-title>ProceedingsoftheIEEESymposiumonEmergingTechnologies,2005.</alt-title></titles><pages>204-208</pages><keywords><keyword>fuzzylogic</keyword><keyword>inferencemechanisms</keyword><keyword>patternrecognition</keyword><keyword>radarcomputing</keyword><keyword>radarsignalprocessing</keyword><keyword>emitterrecognition</keyword><keyword>fuzzyinferencesystem</keyword><keyword>radarsignals</keyword><keyword>atmosphericeffects</keyword><keyword>equipmentnoise</keyword><keyword>radarparametermeasurement</keyword><keyword>patternrecognitionproblem</keyword><keyword>multidimensionalspace</keyword><keyword>dataassociationtools</keyword><keyword>trainingdatarequirements</keyword><keyword>Fuzzysystems</keyword><keyword>Spaceborneradar</keyword><keyword>Marinevehicles</keyword><keyword>Aircraft</keyword><keyword>Dispersion</keyword><keyword>Atmosphericmeasurements</keyword><keyword>Radarmeasurements</keyword><keyword>Extraterrestrialmeasurements</keyword><keyword>Trainingdata</keyword></keywords><dates><year>2005</year><pub-dates><date>18-18Sept.2005</date></pub-dates></dates><urls></urls><electronic-resource-num>10.1109/ICET.2005.1558881</electronic-resource-num></record></Cite></EndNote>[\o"Hassan,2005#179"2]提出一类用于多维空间模式识别的模糊推理系统，该系统可以有效提高辐射源个体识别技术；文献ADDINEN.CITE<EndNote><Cite><Author>Zhang</Author><Year>2005</Year><RecNum>152</RecNum><DisplayText><styleface="superscript">[3]</style></DisplayText><record><rec-number>152</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1535552879">152</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Zhang,Gexiang</author></authors></contributors><titles><title>ResemblanceCoefficientBasedFeatureSelectionAlgorithmforRadarEmitterSignalRecognition</title><secondary-title>SignalProcessing</secondary-title></titles><periodical><full-title>SignalProcessing</full-title></periodical><volume>21</volume><number>6</number><dates><year>2005</year></dates><urls></urls></record></Cite></EndNote>[\o"Zhang,2005#152"3]提出一种在特征选择方法中进行改进，基于相似系数优化特征提取过程；文献ADDINEN.CITE<EndNote><Cite><Author>Xin</Author><Year>2006</Year><RecNum>180</RecNum><DisplayText><styleface="superscript">[4]</style></DisplayText><record><rec-number>180</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1535592890">180</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>G.Xin</author><author>Y.Xiao</author><author>S.Yingfeng</author><author>H.You</author></authors></contributors><titles><title>ANewRadarEmitterRecognitionMethodBasedonVariablePrecisionRoughSetModel</title><secondary-title>2006CIEInternationalConferenceonRadar</secondary-title><alt-title>2006CIEInternationalConferenceonRadar</alt-title></titles><pages>1-4</pages><keywords><keyword>decisiontheory</keyword><keyword>radardetection</keyword><keyword>radarequipment</keyword><keyword>radartargetrecognition</keyword><keyword>roughsettheory</keyword><keyword>sensorfusion</keyword><keyword>radaremitterrecognitionmethod</keyword><keyword>variableprecisionroughsetmodel</keyword><keyword>radaremitterinformation</keyword><keyword>multisensorsystem</keyword><keyword>decisionrule</keyword><keyword>extractedindexdata</keyword><keyword>metricalradarcharacteristicparameter</keyword><keyword>Reconnaissance</keyword><keyword>Radarapplications</keyword><keyword>Intelligentsensors</keyword><keyword>Databases</keyword><keyword>Sun</keyword><keyword>Helium</keyword><keyword>Aerospaceengineering</keyword><keyword>Multisensorsystems</keyword><keyword>Uncertainty</keyword><keyword>radaremitterrecognition</keyword><keyword>decisionrules</keyword><keyword>variableprecisionroughset</keyword></keywords><dates><year>2006</year><pub-dates><date>16-19Oct.2006</date></pub-dates></dates><urls></urls><electronic-resource-num>10.1109/ICR.2006.343200</electronic-resource-num></record></Cite></EndNote>[\o"Xin,2006#180"4]利用基于模糊集决策模型改进辐射源分类器设计，有效提升了信号分类精度。传统的辐射源识别大多只能分析信号差异较大的辐射源，目前研究深入之后可区分差异较小的设备，即特定辐射源识别（SEI），特定辐射源识别与传统辐射源识别的过程大致相同，不过与传统辐射源识别对辐射源常规特征进行提取识别不同，辐射源不同个体由于硬件老化以及非线性差异等原因，即使同一型号同一批次的设备，也会呈现细微的差距，SEI技术更倾向于对截获的信号中无意调制特征进行提取，并与对应载体进行匹配识别。目前关于SEI的研究多从时频分析、高阶谱分析、变分模态分解等方法入手。在时频分析方面，时频分析技术通过对信号时频分布的分析，获得信号时频关系，因此常作为信号无意调制特征提取的中间步骤。文献ADDINEN.CITE<EndNote><Cite><Author>李天琪</Author><Year>2020</Year><RecNum>342</RecNum><DisplayText><styleface="superscript">[5]</style></DisplayText><record><rec-number>342</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1599725904">342</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>李天琪</author><author>张玉</author><author>张进</author><author>唐波</author></authors></contributors><auth-address>国防科技大学电子对抗学院;</auth-address><titles><title>基于时频与快速熵的IFF辐射源个体识别方法</title><secondary-title>探测与控制学报</secondary-title></titles><periodical><full-title>探测与控制学报</full-title></periodical><pages>87-93+103</pages><volume>42</volume><number>01</number><keywords><keyword>敌我识别</keyword><keyword>辐射源个体识别</keyword><keyword>时频分析</keyword><keyword>样本熵</keyword></keywords><dates><year>2020</year></dates><isbn>1008-1194</isbn><call-num>61-1316/TJ</call-num><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[\o"李天琪,2020#342"5]提出一种基于时间尺度分解与快速熵的特定辐射源识别技术；文献ADDINEN.CITE<EndNote><Cite><Author>张玉</Author><Year>2020</Year><RecNum>343</RecNum><DisplayText><styleface="superscript">[6]</style></DisplayText><record><rec-number>343</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1599726080">343</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>张玉</author><author>李天琪</author><author>张进</author><author>唐波</author></authors></contributors><auth-address>国防科技大学电子对抗学院;</auth-address><titles><title>基于集成固有时间尺度分解的IFF辐射源个体识别算法</title><secondary-title>电子与信息学报</secondary-title></titles><periodical><full-title>电子与信息学报</full-title></periodical><pages>430-437</pages><volume>42</volume><number>02</number><keywords><keyword>图像处理</keyword><keyword>敌我识别</keyword><keyword>辐射源个体识别</keyword><keyword>时频分析</keyword></keywords><dates><year>2020</year></dates><isbn>1009-5896</isbn><call-num>11-4494/TN</call-num><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[\o"张玉,2020#343"6]针对辐射源识别技术中信号无意调制特征比较细微的原理，将信号划分为不同的模态，对每个模态进行时频图分析其纹理特征，将图像作为输入信号；文献ADDINEN.CITE<EndNote><Cite><Author>Chunyun</Author><Year>2010</Year><RecNum>155</RecNum><DisplayText><styleface="superscript">[7]</style></DisplayText><record><rec-number>155</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1535552955">155</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Chunyun,Song</author><author>Jianmin,Xu</author><author>Yi,Zhan</author></authors></contributors><titles><title>Amethodforspecificemitteridentificationbasedonempiricalmodedecomposition</title><secondary-title>2010IEEEInternationalConferenceonWirelessCommunications,NetworkingandInformationSecurity</secondary-title><alt-title>2010IEEEInternationalConferenceonWirelessCommunications,NetworkingandInformationSecurity</alt-title></titles><pages>54-57</pages><keywords><keyword>identification</keyword><keyword>signalprocessing</keyword><keyword>time-varyingnetworks</keyword><keyword>specificemitteridentification</keyword><keyword>empiricalmodedecomposition</keyword><keyword>radiosignals</keyword><keyword>radarsignals</keyword><keyword>nonlineartimeseries</keyword><keyword>timedomain</keyword><keyword>Waveletdistribution</keyword><keyword>Wigner-Villedistribution</keyword><keyword>Timedomainanalysis</keyword><keyword>Frequencyestimation</keyword><keyword>Waveletanalysis</keyword><keyword>Radiotransmitters</keyword><keyword>Biomedicalmeasurements</keyword><keyword>Radar</keyword><keyword>Spectralanalysis</keyword><keyword>Timeseriesanalysis</keyword><keyword>Parameterestimation</keyword><keyword>transients</keyword><keyword>non-stationary</keyword></keywords><dates><year>2010</year><pub-dates><date>25-27June2010</date></pub-dates></dates><urls></urls><electronic-resource-num>10.1109/WCINS.2010.5541885</electronic-resource-num></record></Cite></EndNote>[\o"Chunyun,2010#155"7]结合经验模态分解技术，对辐射源信号进行模态分解而后对子模态进行时频估计；文献ADDINEN.CITE<EndNote><Cite><Author>Zhang</Author><Year>2016</Year><RecNum>159</RecNum><DisplayText><styleface="superscript">[8]</style></DisplayText><record><rec-number>159</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1535553041">159</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>J.Zhang</author><author>F.Wang</author><author>O.A.Dobre</author><author>Z.Zhong</author></authors></contributors><titles><title>SpecificEmitterIdentificationviaHilbert–HuangTransforminSingle-HopandRelayingScenarios</title><secondary-title>IEEETransactionsonInformationForensicsandSecurity</secondary-title></titles><periodical><full-title>IEEETransactionsonInformationForensicsandSecurity</full-title></periodical><pages>1192-1205</pages><volume>11</volume><number>6</number><keywords><keyword>Hilberttransforms</keyword><keyword>poweramplifiers</keyword><keyword>signalprocessing</keyword><keyword>specificemitteridentification</keyword><keyword>Hilbert-Huangtransform</keyword><keyword>relayingscenarios</keyword><keyword>single-hopscenarios</keyword><keyword>SEIproblem</keyword><keyword>Hilbertspectrum</keyword><keyword>identificationfeature</keyword><keyword>Fisher'sdiscriminantratio</keyword><keyword>additivewhiteGaussiannoise</keyword><keyword>relayfingerprints</keyword><keyword>Featureextraction</keyword><keyword>Time-frequencyanalysis</keyword><keyword>Transforms</keyword><keyword>Supportvectormachines</keyword><keyword>Transientanalysis</keyword><keyword>Relays</keyword><keyword>Steady-state</keyword><keyword>Relay</keyword><keyword>Specificemitteridentification(SEI)</keyword></keywords><dates><year>2016</year></dates><isbn>1556-6013</isbn><urls></urls><electronic-resource-num>10.1109/TIFS.2016.2520908</electronic-resource-num></record></Cite></EndNote>[\o"Zhang,2016#159"8]提出了基于HHT变换的辐射源时频分布特征提取算法，对信号的能量熵和高阶矩等特征进行了提取，实验表明分类效果得到有效。高阶统计分析方向：高阶统计量主要指利用辐射源信号高阶谱分析技术，对从时域和频域的角度出发，从信号的高阶矩谱、功率谱、高阶累积量谱进行统计分析，目前在SEI领域中常使用的为高阶累积量谱，也称为高阶谱分析。与传统的信号功率谱分析不同，高阶谱能够从联合分析信号幅度和相位，而且因为信道中白噪声的经高阶谱变换后累计量为零，因此可以有效的滤除信道噪声对信号的影响，因此对于非线性非平稳非高斯信号具有较大优势。考虑到运算量和效率等问题，实际中常用信号3阶高阶谱，也被称为双谱。文献ADDINEN.CITE<EndNote><Cite><Author>王占领</Author><Year>2014</Year><RecNum>52</RecNum><DisplayText><styleface="superscript">[9]</style></DisplayText><record><rec-number>52</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1489633756">52</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>王占领</author><author>张登福</author><author>王世强</author></authors></contributors><auth-address>空军工程大学航空航天工程学院;93986部队;</auth-address><titles><title>雷达辐射源信号双谱二次特征提取方法</title><secondary-title>空军工程大学学报(自然科学版)</secondary-title></titles><periodical><full-title>空军工程大学学报(自然科学版)</full-title></periodical><pages>48-52</pages><volume>15</volume><number>01</number><keywords><keyword>高阶谱分析</keyword><keyword>双谱</keyword><keyword>雷达辐射源信号</keyword><keyword>特征提取</keyword><keyword>灰度共生矩阵</keyword></keywords><dates><year>2014</year></dates><isbn>1009-3516</isbn><call-num>61-1338/N</call-num><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[\o"王占领,2014#52"9]提出一种利用信号灰度共生矩阵的辐射源识别方法；文献ADDINEN.CITE<EndNote><Cite><Author>Yao</Author><Year>2020</Year><RecNum>368</RecNum><DisplayText><styleface="superscript">[10]</style></DisplayText><record><rec-number>368</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1622555843">368</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Y.Yao</author><author>L.Yu</author><author>Y.Chen</author></authors></contributors><titles><title>SpecificEmitterIdentificationBasedonSquareIntegralBispectrumFeatures</title><secondary-title>2020IEEE20thInternationalConferenceonCommunicationTechnology(ICCT)</secondary-title><alt-title>2020IEEE20thInternationalConferenceonCommunicationTechnology(ICCT)</alt-title></titles><pages>1311-1314</pages><dates><year>2020</year><pub-dates><date>28-31Oct.2020</date></pub-dates></dates><isbn>2576-7828</isbn><urls></urls><electronic-resource-num>10.1109/ICCT50939.2020.9295681</electronic-resource-num></record></Cite></EndNote>[\o"Yao,2020#368"10]在研究信号时频分析基础上，提出一种基于平方积分双谱特征方法，有效降低信号特征维度提取计算量；文献ADDINEN.CITE<EndNote><Cite><Author>Ekramul</Author><Year>2014</Year><RecNum>370</RecNum><DisplayText><styleface="superscript">[11]</style></DisplayText><record><rec-number>370</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1622595766">370</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>M.H.Ekramul</author><author>W.A.Jassim</author><author>M.S.A.Zilany</author></authors></contributors><titles><title>Effectsofnoiseonthefeaturesofbispectrum</title><secondary-title>2014IEEE19thInternationalFunctionalElectricalStimulationSocietyAnnualConference(IFESS)</secondary-title><alt-title>2014IEEE19thInternationalFunctionalElectricalStimulationSocietyAnnualConference(IFESS)</alt-title></titles><pages>1-4</pages><dates><year>2014</year><pub-dates><date>17-19Sept.2014</date></pub-dates></dates><urls></urls><electronic-resource-num>10.1109/IFESS.2014.7036758</electronic-resource-num></record></Cite></EndNote>[\o"Ekramul,2014#370"11]提出利用高阶频谱（HOS）分析研究噪声对双谱统计特征的影响，检测信号线性、平稳性偏差；文献ADDINEN.CITE<EndNote><Cite><Author>Mei</Author><Year>2012</Year><RecNum>371</RecNum><DisplayText><styleface="superscript">[12]</style></DisplayText><record><rec-number>371</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1622596027">371</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>J.Mei</author><author>L.Qiao</author><author>Y.Xiao</author></authors></contributors><titles><title>Weakfaultfeatureextractionofgearbox'sbearingbasedonEMD-bispectrum</title><secondary-title>20129thInternationalConferenceonFuzzySystemsandKnowledgeDiscovery</secondary-title><alt-title>20129thInternationalConferenceonFuzzySystemsandKnowledgeDiscovery</alt-title></titles><pages>1439-1443</pages><dates><year>2012</year><pub-dates><date>29-31May2012</date></pub-dates></dates><urls></urls><electronic-resource-num>10.1109/FSKD.2012.6233990</electronic-resource-num></record></Cite></EndNote>[\o"Mei,2012#371"12]提出了一种EMD-双谱方法，将振动信号分解为一系列模态，消除交叉分量有效的降低噪声影响；文献ADDINEN.CITE<EndNote><Cite><Author>Lin</Author><Year>2020</Year><RecNum>369</RecNum><DisplayText><styleface="superscript">[13]</style></DisplayText><record><rec-number>369</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1622556401">369</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Y.Lin</author><author>J.Jia</author><author>S.Wang</author><author>B.Ge</author><author>S.Mao</author></authors></contributors><titles><title>WirelessDeviceIdentificationBasedonRadioFrequencyFingerprintFeatures</title><secondary-title>ICC2020-2020IEEEInternationalConferenceonCommunications(ICC)</secondary-title><alt-title>ICC2020-2020IEEEInternationalConferenceonCommunications(ICC)</alt-title></titles><pages>1-6</pages><dates><year>2020</year><pub-dates><date>7-11June2020</date></pub-dates></dates><isbn>1938-1883</isbn><urls></urls><electronic-resource-num>10.1109/ICC40277.2020.9149226</electronic-resource-num></record></Cite></EndNote>[\o"Lin,2020#369"13]研究辐射源信号稳态特征与暂态特征差异后，基于功率谱密度和分数阶傅里叶变换的基础上对双谱分析方法进行改进，提高了基于稳态特征的辐射源分类精度；文献ADDINEN.CITE<EndNote><Cite><Author>Kang</Author><Year>2016</Year><RecNum>143</RecNum><DisplayText><styleface="superscript">[14]</style></DisplayText><record><rec-number>143</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1535507437">143</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>N.Kang</author><author>M.He</author><author>J.Han</author><author>B.Wang</author></authors></contributors><titles><title>RadaremitterfingerprintrecognitionbasedonbispectrumandSURFfeature</title><secondary-title>2016CIEInternationalConferenceonRadar(RADAR)</secondary-title><alt-title>2016CIEInternationalConferenceonRadar(RADAR)</alt-title></titles><pages>1-5</pages><keywords><keyword>electronicwarfare</keyword><keyword>featureextraction</keyword><keyword>fingerprintidentification</keyword><keyword>militaryradar</keyword><keyword>radarsignalprocessing</keyword><keyword>bispectrumtheory</keyword><keyword>SURFfeature</keyword><keyword>bispectrumprojection</keyword><keyword>radaremitterfingerprintrecognition</keyword><keyword>radaremitteridentification</keyword><keyword>radarsignals</keyword><keyword>Radar</keyword><keyword>Fingerprintrecognition</keyword><keyword>Gaussiannoise</keyword><keyword>Spectrogram</keyword><keyword>Transforms</keyword><keyword>Time-frequencyanalysis</keyword><keyword>bispectrum</keyword></keywords><dates><year>2016</year><pub-dates><date>10-13Oct.2016</date></pub-dates></dates><urls></urls><electronic-resource-num>10.1109/RADAR.2016.8059588</electronic-resource-num></record></Cite></EndNote>[\o"Kang,2016#143"14]利用投影灰度图，将图像作为辐射源信号的表征方法，对信号进行分类识别；文献ADDINEN.CITE<EndNote><Cite><Author>王书豪</Author><Year>2019</Year><RecNum>367</RecNum><DisplayText><styleface="superscript">[15]</style></DisplayText><record><rec-number>367</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1622555339">367</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>王书豪</author><author>阮怀林</author></authors></contributors><auth-address>国防科技大学电子对抗学院;</auth-address><titles><title>基于切片双谱多重分形特征的雷达信号识别算法</title><secondary-title>探测与控制学报</secondary-title></titles><periodical><full-title>探测与控制学报</full-title></periodical><pages>66-70</pages><volume>41</volume><number>05</number><keywords><keyword>切片双谱</keyword><keyword>多重分形</keyword><keyword>广义维数</keyword><keyword>支持向量机</keyword></keywords><dates><year>2019</year></dates><isbn>1008-1194</isbn><call-num>61-1316/TJ</call-num><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[\o"王书豪,2019#367"15]针对现有算法对噪声敏感，提出基于信号广义维数和多重分形谱特征的辐射源识别方法。变分模态分解（VariationalModeDecomposition,VMD）ADDINEN.CITE<EndNote><Cite><Author>Dragomiretskiy</Author><Year>2014</Year><RecNum>328</RecNum><DisplayText><styleface="superscript">[16]</style></DisplayText><record><rec-number>328</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1598002548">328</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>K.Dragomiretskiy</author><author>D.Zosso</author></authors></contributors><titles><title>VariationalModeDecomposition</title><secondary-title>IEEETransactionsonSignalProcessing</secondary-title></titles><periodical><full-title>IEEETransactionsonSignalProcessing</full-title></periodical><pages>531-544</pages><volume>62</volume><number>3</number><keywords><keyword>signaldenoising</keyword><keyword>Wienerfilters</keyword><keyword>variationalmodedecomposition</keyword><keyword>empiricalmodedecomposition</keyword><keyword>EMD</keyword><keyword>decompositionproblem</keyword><keyword>synchrosqueezing</keyword><keyword>empiricalwavelets</keyword><keyword>recursivevariationaldecomposition</keyword><keyword>nonrecursivevariationalmode</keyword><keyword>Fourierdomain</keyword><keyword>narrow-bandprior</keyword><keyword>Wienerfilterdenoising</keyword><keyword>Frequencyestimation</keyword><keyword>Frequencymodulation</keyword><keyword>Bandwidth</keyword><keyword>Noise</keyword><keyword>Robustness</keyword><keyword>Wavelettransforms</keyword><keyword>AM-FM</keyword><keyword>augmentedLagrangian</keyword><keyword>Fouriertransform</keyword><keyword>Hilberttransform</keyword><keyword>modedecomposition</keyword><keyword>spectraldecomposition</keyword><keyword>variationalproblem</keyword><keyword>Wienerfilter</keyword></keywords><dates><year>2014</year></dates><isbn>1941-0476</isbn><urls></urls><electronic-resource-num>10.1109/TSP.2013.2288675</electronic-resource-num></record></Cite></EndNote>[\o"Dragomiretskiy,2014#328"16]是学者在EMD的研究基础上提出的自适应时频分析分析方法。与EMD分解的结果不同，VMD通过引入拉格朗日函数的方法，将信号分解为多个窄幅子模态。文献ADDINEN.CITEADDINEN.CITE.DATA[\o"Gok,2020#305"17]针对辐射源识别提出一种利用脉冲到达时间处理，通过VMD对信号分解后将包络以及相位信息送分类器进行识别分析一；文献ADDINEN.CITE<EndNote><Cite><Author>He</Author><Year>2020</Year><RecNum>303</RecNum><DisplayText><styleface="superscript">[18]</style></DisplayText><record><rec-number>303</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1597213276">303</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>B.He</author><author>F.Wang</author></authors></contributors><titles><title>CooperativeSpecificEmitterIdentificationviaMultipleDistortedReceivers</title><secondary-title>IEEETransactionsonInformationForensicsandSecurity</secondary-title></titles><periodical><full-title>IEEETransactionsonInformationForensicsandSecurity</full-title></periodical><pages>3791-3806</pages><volume>15</volume><keywords><keyword>Receivers</keyword><keyword>Featureextraction</keyword><keyword>Distortion</keyword><keyword>Radiotransmitters</keyword><keyword>Transientanalysis</keyword><keyword>Time-frequencyanalysis</keyword><keyword>Transforms</keyword><keyword>Empiricalmodedecomposition(EMD)</keyword><keyword>intrinsictime-scaledecomposition(ITD)</keyword><keyword>receiverdistortion</keyword><keyword>specificemitteridentification</keyword><keyword>variationalmodedecomposition(VMD)</keyword></keywords><dates><year>2020</year></dates><isbn>1556-6021</isbn><urls></urls><electronic-resource-num>10.1109/TIFS.2020.3001721</electronic-resource-num></record></Cite></EndNote>[\o"He,2020#303"18]提出基于信号分解的分别基于内在时间尺度分解（ITD）和变分模式分解（VMD）的两种方案，并利用支持向量机进行分类识别，实现了在衰落信道多接收机协作的特定辐射源识别；文献ADDINEN.CITE<EndNote><Cite><Author>王振威</Author><Year>2015</Year><RecNum>340</RecNum><DisplayText><styleface="superscript">[19]</style></DisplayText><record><rec-number>340</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1599724745">340</key></foreign-keys><ref-typename="Thesis">32</ref-type><contributors><authors><author>王振威</author></authors><tertiary-authors><author>姜万录,</author></tertiary-authors></contributors><titles><title>基于变分模态分解的故障诊断方法研究</title></titles><keywords><keyword>机械设备</keyword><keyword>故障识别</keyword><keyword>混沌粒子群</keyword><keyword>变分模态分解</keyword><keyword>特征提取</keyword><keyword>核模糊C均值聚类</keyword></keywords><dates><year>2015</year></dates><publisher>燕山大学</publisher><work-type>硕士</work-type><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[\o"王振威,2015#340"19]提出一种基于混沌粒子群理论的VMD改进算法，选取各个子模态包络为基准，确定分解参数最佳组合；文献ADDINEN.CITE<EndNote><Cite><Author>Biswal</Author><Year>2019</Year><RecNum>372</RecNum><DisplayText><styleface="superscript">[20]</style></DisplayText><record><rec-number>372</rec-number><foreign-keys><keyapp="EN"db-id="r5trxtety5vvprefwdqv9sar5azr2vewp9r5"timestamp="1622596219">372</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>G.Biswal</author><author>A.B.Kambhampati</author><author>B.Ramkumar</author><author>M.S.Manikandan</author></authors></contributors><titles><title>SpecificEmitterIdentificationOverFadingChannels</title><secondary-title>2019InternationalConferenceonRangeTechnology(ICORT)</secondary-title><alt-title>2019InternationalConferenceonRangeTechnology(ICORT)</alt-title></titles><pages>1-5</pages><dates><year>2019</year><pub-dates><date>15-17Feb.2019</date></pub-dates></dates><urls></urls><electronic-resource-num>10.1109/ICORT46471.2019.9069647</electronic-resource-num></record></Cite></EndNote>[\o"Biswal,2019#372"20]通过研究特定辐射源非线性特性，对衰落信道上信号的VMD能量熵、一阶矩和相关系数等特征进行了提取；文献ADDINEN.CITE<EndNote><Cite><Author>Gok</Author><Year>2017</Year><RecNum>373</RecNum><DisplayText><styleface="superscript">[21]</style></DisplayText><record><rec-number>373</rec-number><foreign-keys