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组合建模的海上机动目标群威胁预警方法
Title:ThreatAwarenessMethodsfortheCombinedModelingofNavalMobileTargetGroups
Abstract:
Navaloperationsinthemaritimedomaininvolveamultitudeofthreats,includingthoseposedbymobiletargetgroups.Detectingandassessingthepotentialthreatsinreal-timeiscrucialforensuringthesafetyofnavalforces.Thispaperfocusesonthedevelopmentofthreatawarenessmethodsbasedoncombinedmodelingforeffectivepredictionandearlywarningofthreatspresentedbymobiletargetgroupsinthemaritimedomain.Theproposedmethodsleveragedatafromvarioussources,suchasradar,sonar,andintelligencereports,toenablecomprehensivethreatanalysis.Experimentalevaluationsutilizingreal-worlddatademonstratetheeffectivenessoftheproposedmethodsinachievingtimelyandaccuratethreatawareness.
1.Introduction
Themaritimedomainpresentsuniquechallengesforthreatawarenessduetothevastnessoftheareaandthemobilityofnavaltargets.Traditionalthreatawarenessmethodsrelyonindividualsensorsystems,whichoftenresultininformationfragmentationandlimitedsituationalawareness.Toaddressthislimitation,thereisaneedforacomprehensiveapproachthatcombinesdatafrommultiplesourcesandusesadvancedmodelingtechniquestoimprovethreatawarenessinthemaritimedomain.
2.DataSourcesforThreatAwareness
Thefirststepindevelopingeffectivethreatawarenessmethodsistoidentifyandintegraterelevantdatasources.Thissectiondiscussesthekeydatasourcesforthreatawareness,includingradar,sonar,andintelligencereports.Eachofthesesourcesprovidesuniqueinformationaboutthecharacteristicsandthreatsposedbymobiletargetgroups.
3.CombinedModelingFramework
Toachievecomprehensivethreatawareness,acombinedmodelingframeworkisproposed.Thisframeworkintegratesdatafrommultiplesourcesandusesadvancedmodelingtechniquessuchasdatafusion,machinelearning,andpredictiveanalytics.Thecombinationofthesemodelingtechniquesenablesthedevelopmentofrobustthreatawarenessmodelsthatcanadapttoever-changingmaritimescenarios.
4.DataFusionTechniques
Datafusionplaysacrucialroleincombiningdatafromdifferentsources.Thissectionpresentsvariousdataf