《輻射源信號識別探究的國內外文獻綜述》4500字

輻射源信號識別研究的國內外文獻綜述1輻射源個體識別研究現狀特定輻射源識別技術（SpecificEmitterIdentification，SEI）是由美國軍隊率先進行研究并確立相關概念，我國在該領域起步較晚，從20世紀80年代開始研究至今已有四十余年的發(fā)展與改進，總體框架已然確立，如REF_Ref50928055\h圖11所示，但技術細節(jié)仍在不斷發(fā)展。最開始雷達輻射源識別技術比較接近于傳統(tǒng)信號處理，如文獻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]對輻射源信號常規(guī)特征進行分析研究，較少了電子對抗偵察系統(tǒng)的組成；文獻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]提出一類用于多維空間模式識別的模糊推理系統(tǒng)，該系統(tǒng)可以有效提高輻射源個體識別技術；文獻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]提出一種在特征選擇方法中進行改進，基于相似系數優(yōu)化特征提取過程；文獻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]利用基于模糊集決策模型改進輻射源分類器設計，有效提升了信號分類精度。傳統(tǒng)的輻射源識別大多只能分析信號差異較大的輻射源，目前研究深入之后可區(qū)分差異較小的設備，即特定輻射源識別（SEI），特定輻射源識別與傳統(tǒng)輻射源識別的過程大致相同，不過與傳統(tǒng)輻射源識別對輻射源常規(guī)特征進行提取識別不同，輻射源不同個體由于硬件老化以及非線性差異等原因，即使同一型號同一批次的設備，也會呈現細微的差距，SEI技術更傾向于對截獲的信號中無意調制特征進行提取，并與對應載體進行匹配識別。目前關于SEI的研究多從時頻分析、高階譜分析、變分模態(tài)分解等方法入手。在時頻分析方面，時頻分析技術通過對信號時頻分布的分析，獲得信號時頻關系，因此常作為信號無意調制特征提取的中間步驟。文獻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>張玉</author><author>張進</author><author>唐波</author></authors></contributors><auth-address>國防科技大學電子對抗學院;</auth-address><titles><title>基于時頻與快速熵的IFF輻射源個體識別方法</title><secondary-title>探測與控制學報</secondary-title></titles><periodical><full-title>探測與控制學報</full-title></periodical><pages>87-93+103</pages><volume>42</volume><number>01</number><keywords><keyword>敵我識別</keyword><keyword>輻射源個體識別</keyword><keyword>時頻分析</keyword><keyword>樣本熵</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]提出一種基于時間尺度分解與快速熵的特定輻射源識別技術；文獻ADDINEN.CITE<EndNote><Cite><Author>張玉</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>張玉</author><author>李天琪</author><author>張進</author><author>唐波</author></authors></contributors><auth-address>國防科技大學電子對抗學院;</auth-address><titles><title>基于集成固有時間尺度分解的IFF輻射源個體識別算法</title><secondary-title>電子與信息學報</secondary-title></titles><periodical><full-title>電子與信息學報</full-title></periodical><pages>430-437</pages><volume>42</volume><number>02</number><keywords><keyword>圖像處理</keyword><keyword>敵我識別</keyword><keyword>輻射源個體識別</keyword><keyword>時頻分析</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"張玉,2020#343"6]針對輻射源識別技術中信號無意調制特征比較細微的原理，將信號劃分為不同的模態(tài)，對每個模態(tài)進行時頻圖分析其紋理特征，將圖像作為輸入信號；文獻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]結合經驗模態(tài)分解技術，對輻射源信號進行模態(tài)分解而后對子模態(tài)進行時頻估計；文獻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變換的輻射源時頻分布特征提取算法，對信號的能量熵和高階矩等特征進行了提取，實驗表明分類效果得到有效。高階統(tǒng)計分析方向：高階統(tǒng)計量主要指利用輻射源信號高階譜分析技術，對從時域和頻域的角度出發(fā)，從信號的高階矩譜、功率譜、高階累積量譜進行統(tǒng)計分析，目前在SEI領域中常使用的為高階累積量譜，也稱為高階譜分析。與傳統(tǒng)的信號功率譜分析不同，高階譜能夠從聯(lián)合分析信號幅度和相位，而且因為信道中白噪聲的經高階譜變換后累計量為零，因此可以有效的濾除信道噪聲對信號的影響，因此對于非線性非平穩(wěn)非高斯信號具有較大優(yōu)勢?？紤]到運算量和效率等問題，實際中常用信號3階高階譜，也被稱為雙譜。文獻ADDINEN.CITE<EndNote><Cite><Author>王占領</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>王占領</author><author>張登福</author><author>王世強</author></authors></contributors><auth-address>空軍工程大學航空航天工程學院;93986部隊;</auth-address><titles><title>雷達輻射源信號雙譜二次特征提取方法</title><secondary-title>空軍工程大學學報(自然科學版)</secondary-title></titles><periodical><full-title>空軍工程大學學報(自然科學版)</full-title></periodical><pages>48-52</pages><volume>15</volume><number>01</number><keywords><keyword>高階譜分析</keyword><keyword>雙譜</keyword><keyword>雷達輻射源信號</keyword><keyword>特征提取</keyword><keyword>灰度共生矩陣</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"王占領,2014#52"9]提出一種利用信號灰度共生矩陣的輻射源識別方法；文獻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]在研究信號時頻分析基礎上，提出一種基于平方積分雙譜特征方法，有效降低信號特征維度提取計算量；文獻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]提出利用高階頻譜（HOS）分析研究噪聲對雙譜統(tǒng)計特征的影響，檢測信號線性、平穩(wěn)性偏差；文獻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]提出了一種EMD-雙譜方法，將振動信號分解為一系列模態(tài)，消除交叉分量有效的降低噪聲影響；文獻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]研究輻射源信號穩(wěn)態(tài)特征與暫態(tài)特征差異后，基于功率譜密度和分數階傅里葉變換的基礎上對雙譜分析方法進行改進，提高了基于穩(wěn)態(tài)特征的輻射源分類精度；文獻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]利用投影灰度圖，將圖像作為輻射源信號的表征方法，對信號進行分類識別；文獻ADDINEN.CITE<EndNote><Cite><Author>王書豪</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>王書豪</author><author>阮懷林</author></authors></contributors><auth-address>國防科技大學電子對抗學院;</auth-address><titles><title>基于切片雙譜多重分形特征的雷達信號識別算法</title><secondary-title>探測與控制學報</secondary-title></titles><periodical><full-title>探測與控制學報</full-title></periodical><pages>66-70</pages><volume>41</volume><number>05</number><keywords><keyword>切片雙譜</keyword><keyword>多重分形</keyword><keyword>廣義維數</keyword><keyword>支持向量機</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"王書豪,2019#367"15]針對現有算法對噪聲敏感，提出基于信號廣義維數和多重分形譜特征的輻射源識別方法。變分模態(tài)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]是學者在EMD的研究基礎上提出的自適應時頻分析分析方法。與EMD分解的結果不同，VMD通過引入拉格朗日函數的方法，將信號分解為多個窄幅子模態(tài)。文獻ADDINEN.CITEADDINEN.CITE.DATA[\o"Gok,2020#305"17]針對輻射源識別提出一種利用脈沖到達時間處理，通過VMD對信號分解后將包絡以及相位信息送分類器進行識別分析一；文獻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]提出基于信號分解的分別基于內在時間尺度分解（ITD）和變分模式分解（VMD）的兩種方案，并利用支持向量機進行分類識別，實現了在衰落信道多接收機協(xié)作的特定輻射源識別；文獻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>姜萬錄,</author></tertiary-authors></contributors><titles><title>基于變分模態(tài)分解的故障診斷方法研究</title></titles><keywords><keyword>機械設備</keyword><keyword>故障識別</keyword><keyword>混沌粒子群</keyword><keyword>變分模態(tài)分解</keyword><keyword>特征提取</keyword><keyword>核模糊C均值聚類</keyword></keywords><dates><year>2015</year></dates><publisher>燕山大學</publisher><work-type>碩士</work-type><urls></urls><remote-database-provider>Cnki</remote-database-provider></record></Cite></EndNote>[\o"王振威,2015#340"19]提出一種基于混沌粒子群理論的VMD改進算法，選取各個子模態(tài)包絡為基準，確定分解參數最佳組合；文獻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]通過研究特定輻射源非線性特性，對衰落信道上信號的VMD能量熵、一階矩和相關系數等特征進行了提??；文獻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