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基于AlexNet的雷达干扰识别方法研究 Abstract Radarinterferenceisacommonprobleminmodernradarsystems,itcanseverelydegradetheperformanceofradarsystemsandevencausefailureinmission-criticalapplications.Inthispaper,weproposearadarinterferencerecognitionmethodbasedontheAlexNetdeeplearningarchitecture.Theproposedmethodusesadatasetofradarsignalscontaminatedwithdifferenttypesofinterference,whichisusedtotraintheAlexNetmodeltorecognizedifferenttypesofinterference.Theresultsofexperimentsshowthattheproposedmethodachieveshighaccuracyinidentifyingdifferenttypesofradarinterferenceinareal-timeandautomaticmanner. Introduction Radarisanessentialtechnologyusedforsensingandsurveillanceinvariousapplications,includingairtrafficcontrol,militaryoperations,weatherforecasting,andnavigation.Inrecentyears,withtherapiddevelopmentofelectronicwarfaretechnology,radarsystemsfaceincreasinglydiverseandcomplexformsofinterference,suchasclutter,jamming,anddeception.Interferencecancauseseriousproblemsforradarsystems,suchasreduceddetectionrange,inaccuratetargetpositioning,andevenfalsealarms.Therefore,interferencerecognitioninradarsystemshasbecomeacriticaltaskforensuringreliableandeffectiveoperations. Deeplearninghasshowngreatpotentialinvariousfields,includingcomputervision,speechrecognition,naturallanguageprocessing,andsignalprocessing.Inrecentyears,deeplearninghasbeenwidelyusedinradarsignalprocessing,particularlyinradartargetrecognition.However,theapplicationofdeeplearninginradarinterferencerecognitionhasnotbeenextensivelystudiedyet.Inthispaper,weproposearadarinterferencerecognitionmethodbasedontheAlexNetdeeplearningarchitecture. AlexNetisoneofthemostwidelyuseddeeplearningarchitectures,whichhasachievedoutstandingresultsinvariouscomputervisiontasks,suchasimageclassification,objectdetection,andsemanticsegmentation.AlexNetconsistsofseveralconvolutionallayersandpoolinglayers,followedbyfullyconnectedlayers,whichcanextractusefulfeaturesfrominputimagesandclassifythemintodifferentcategories. ProposedMethod Theproposedmethodconsi