《輻射源信號(hào)識(shí)別探究的國內(nèi)外文獻(xiàn)綜述》4500字

輻射源信號(hào)識(shí)別研究的國內(nèi)外文獻(xiàn)綜述1輻射源個(gè)體識(shí)別研究現(xiàn)狀特定輻射源識(shí)別技術(shù)（SpecificEmitterIdentification，SEI）是由美國軍隊(duì)率先進(jìn)行研究并確立相關(guān)概念，我國在該領(lǐng)域起步較晚，從20世紀(jì)80年代開始研究至今已有四十余年的發(fā)展與改進(jìn)，總體框架已然確立，如REF_Ref50928055\h圖11所示，但技術(shù)細(xì)節(jié)仍在不斷發(fā)展。最開始雷達(dá)輻射源識(shí)別技術(shù)比較接近于傳統(tǒng)信號(hào)處理，如文獻(xiàn)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(duì)輻射源信號(hào)常規(guī)特征進(jìn)行分析研究，較少了電子對(duì)抗偵察系統(tǒng)的組成；文獻(xiàn)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(shí)別的模糊推理系統(tǒng)，該系統(tǒng)可以有效提高輻射源個(gè)體識(shí)別技術(shù)；文獻(xiàn)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(jìn)行改進(jìn)，基于相似系數(shù)優(yōu)化特征提取過程；文獻(xiàn)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(jìn)輻射源分類器設(shè)計(jì)，有效提升了信號(hào)分類精度。傳統(tǒng)的輻射源識(shí)別大多只能分析信號(hào)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(shè)備，即特定輻射源識(shí)別（SEI），特定輻射源識(shí)別與傳統(tǒng)輻射源識(shí)別的過程大致相同，不過與傳統(tǒng)輻射源識(shí)別對(duì)輻射源常規(guī)特征進(jìn)行提取識(shí)別不同，輻射源不同個(gè)體由于硬件老化以及非線性差異等原因，即使同一型號(hào)同一批次的設(shè)備，也會(huì)呈現(xiàn)細(xì)微的差距，SEI技術(shù)更傾向于對(duì)截獲的信號(hào)中無意調(diào)制特征進(jìn)行提取，并與對(duì)應(yīng)載體進(jìn)行匹配識(shí)別。目前關(guān)于SEI的研究多從時(shí)頻分析、高階譜分析、變分模態(tài)分解等方法入手。在時(shí)頻分析方面，時(shí)頻分析技術(shù)通過對(duì)信號(hào)時(shí)頻分布的分析，獲得信號(hào)時(shí)頻關(guān)系，因此常作為信號(hào)無意調(diào)制特征提取的中間步驟。文獻(xiàn)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(jìn)</author><author>唐波</author></authors></contributors><auth-address>國防科技大學(xué)電子對(duì)抗學(xué)院;</auth-address><titles><title>基于時(shí)頻與快速熵的IFF輻射源個(gè)體識(shí)別方法</title><secondary-title>探測(cè)與控制學(xué)報(bào)</secondary-title></titles><periodical><full-title>探測(cè)與控制學(xué)報(bào)</full-title></periodical><pages>87-93+103</pages><volume>42</volume><number>01</number><keywords><keyword>敵我識(shí)別</keyword><keyword>輻射源個(gè)體識(shí)別</keyword><keyword>時(shí)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(shí)間尺度分解與快速熵的特定輻射源識(shí)別技術(shù)；文獻(xiàn)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(jìn)</author><author>唐波</author></authors></contributors><auth-address>國防科技大學(xué)電子對(duì)抗學(xué)院;</auth-address><titles><title>基于集成固有時(shí)間尺度分解的IFF輻射源個(gè)體識(shí)別算法</title><secondary-title>電子與信息學(xué)報(bào)</secondary-title></titles><periodical><full-title>電子與信息學(xué)報(bào)</full-title></periodical><pages>430-437</pages><volume>42</volume><number>02</number><keywords><keyword>圖像處理</keyword><keyword>敵我識(shí)別</keyword><keyword>輻射源個(gè)體識(shí)別</keyword><keyword>時(shí)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(duì)輻射源識(shí)別技術(shù)中信號(hào)無意調(diào)制特征比較細(xì)微的原理，將信號(hào)劃分為不同的模態(tài)，對(duì)每個(gè)模態(tài)進(jìn)行時(shí)頻圖分析其紋理特征，將圖像作為輸入信號(hào)；文獻(xiàn)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(jié)合經(jīng)驗(yàn)?zāi)B(tài)分解技術(shù)，對(duì)輻射源信號(hào)進(jìn)行模態(tài)分解而后對(duì)子模態(tài)進(jìn)行時(shí)頻估計(jì)；文獻(xiàn)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(shí)頻分布特征提取算法，對(duì)信號(hào)的能量熵和高階矩等特征進(jìn)行了提取，實(shí)驗(yàn)表明分類效果得到有效。高階統(tǒng)計(jì)分析方向：高階統(tǒng)計(jì)量主要指利用輻射源信號(hào)高階譜分析技術(shù)，對(duì)從時(shí)域和頻域的角度出發(fā)，從信號(hào)的高階矩譜、功率譜、高階累積量譜進(jìn)行統(tǒng)計(jì)分析，目前在SEI領(lǐng)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(hào)功率譜分析不同，高階譜能夠從聯(lián)合分析信號(hào)幅度和相位，而且因?yàn)樾诺乐邪自肼暤慕?jīng)高階譜變換后累計(jì)量為零，因此可以有效的濾除信道噪聲對(duì)信號(hào)的影響，因此對(duì)于非線性非平穩(wěn)非高斯信號(hào)具有較大優(yōu)勢(shì)。考慮到運(yùn)算量和效率等問題，實(shí)際中常用信號(hào)3階高階譜，也被稱為雙譜。文獻(xiàn)ADDINEN.CITE<EndNote><Cite><Author>王占領(lǐng)</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(lǐng)</author><author>張登福</author><author>王世強(qiáng)</author></authors></contributors><auth-address>空軍工程大學(xué)航空航天工程學(xué)院;93986部隊(duì);</auth-address><titles><title>雷達(dá)輻射源信號(hào)雙譜二次特征提取方法</title><secondary-title>空軍工程大學(xué)學(xué)報(bào)(自然科學(xué)版)</secondary-title></titles><periodical><full-title>空軍工程大學(xué)學(xué)報(bào)(自然科學(xué)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(dá)輻射源信號(hào)</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(lǐng),2014#52"9]提出一種利用信號(hào)灰度共生矩陣的輻射源識(shí)別方法；文獻(xiàn)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(hào)時(shí)頻分析基礎(chǔ)上，提出一種基于平方積分雙譜特征方法，有效降低信號(hào)特征維度提取計(jì)算量；文獻(xiàn)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(duì)雙譜統(tǒng)計(jì)特征的影響，檢測(cè)信號(hào)線性、平穩(wěn)性偏差；文獻(xiàn)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(dòng)信號(hào)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(xiàn)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(hào)穩(wěn)態(tài)特征與暫態(tài)特征差異后，基于功率譜密度和分?jǐn)?shù)階傅里葉變換的基礎(chǔ)上對(duì)雙譜分析方法進(jìn)行改進(jìn)，提高了基于穩(wěn)態(tài)特征的輻射源分類精度；文獻(xiàn)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(hào)的表征方法，對(duì)信號(hào)進(jìn)行分類識(shí)別；文獻(xiàn)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(xué)電子對(duì)抗學(xué)院;</auth-address><titles><title>基于切片雙譜多重分形特征的雷達(dá)信號(hào)識(shí)別算法</title><secondary-title>探測(cè)與控制學(xué)報(bào)</secondary-title></titles><periodical><full-title>探測(cè)與控制學(xué)報(bào)</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(shù)</keyword><keyword>支持向量機(jī)</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(duì)現(xiàn)有算法對(duì)噪聲敏感，提出基于信號(hào)廣義維數(shù)和多重分形譜特征的輻射源識(shí)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(xué)者在EMD的研究基礎(chǔ)上提出的自適應(yīng)時(shí)頻分析分析方法。與EMD分解的結(jié)果不同，VMD通過引入拉格朗日函數(shù)的方法，將信號(hào)分解為多個(gè)窄幅子模態(tài)。文獻(xiàn)ADDINEN.CITEADDINEN.CITE.DATA[\o"Gok,2020#305"17]針對(duì)輻射源識(shí)別提出一種利用脈沖到達(dá)時(shí)間處理，通過VMD對(duì)信號(hào)分解后將包絡(luò)以及相位信息送分類器進(jìn)行識(shí)別分析一；文獻(xiàn)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(hào)分解的分別基于內(nèi)在時(shí)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(jī)進(jìn)行分類識(shí)別，實(shí)現(xiàn)了在衰落信道多接收機(jī)協(xié)作的特定輻射源識(shí)別；文獻(xiàn)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(jī)械設(shè)備</keyword><keyword>故障識(shí)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(xué)</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(jìn)算法，選取各個(gè)子模態(tài)包絡(luò)為基準(zhǔn)，確定分解參數(shù)最佳組合；文獻(xiàn)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(duì)衰落信道上信號(hào)的VMD能量熵、一階矩和相關(guān)系數(shù)等特征進(jìn)行了提?。晃墨I(xiàn)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