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基于组合模型的雷达故障预测分析（英文）.docx

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基于组合模型的雷达故障预测分析（英文） Introduction Radarsystemsplayacrucialroleinmodernmilitaryandcivilapplications.Thesesystemsrelyoncomplextechnologiestooperatesuccessfully,andtheyencountervariouschallengesindifferentenvironments,suchasharshweatherconditionsorelectronicinterference.Faultsinradarsystemscancausesignificantdelayindetectingandtrackingtargets,andthereforedegradetheiroverallperformance.Toavoidsuchnegativeimpacts,radarsystemmaintenanceisessential,anditcanbeperformedbasedonpredictiveanalysis.Inthispaper,weproposeacombinationmodelforradarfaultpredictionanalysisthathelpsmaintenancepersonneldetectfaultsbeforetheyhappen. LiteratureReview Manystudieshavebeenconductedtopredictradarfaults.Oneofthetechniquesusedisthewaveletnetworkmodel.Inthismodel,thesignalisdecomposedintofrequencysubbandsusingthewavelettransform,thenaneuralnetworkisusedtopredictthefaultsbasedonthesubbands.Thewaveletnetworkmodelhasshowngoodperformanceinfaultpredictioninradarsystems(Gaoetal.,2004).Otherstudieshaveproposedaneuralnetworkmodelbasedontheechocharacteristicstopredictradarfaults(Liuetal.,2011;Lietal.,2015).Thesemodelshavealsodemonstratedgoodpredictionperformance. However,thesetechniqueshavelimitationsintermsofpredictionaccuracy.Theycannotcapturethecomplexinteractionsamongdifferentfactorsthatcancauseradarfaults.Inresponsetothislimitation,theproposedcombinationmodelintegratesmultipleapproachestoimprovethepredictionaccuracy. Methodology Theproposedcombinationmodelconsistsofthreestages:datapreprocessing,featureextraction,andfaultprediction. Datapreprocessingisanessentialsteptoeliminateirrelevantorredundantdatathatmayimpedethepredictionaccuracy.Inthisstage,therawdatacollectedfromtheradarsystemispreprocessedusingfilterstoremovenoiseandotherirrelevantsignals.Then,time-domainandfrequency-domainfeaturesareextractedfromthepreprocesseddata. Featureextractionistheprocessofselectingtherelevantfeaturesthataffecttheradar’sperformanceandcanbeusedtopredictfaults.Inthisstage,afeatureselectionalgorithmisusedtoextractthemost