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基于RCS信息的雷达目标大小分类方法
Title:RadarTargetSizeClassificationMethodbasedonRCSInformation
Abstract:
Radarsystemsplayacrucialroleintargetdetectionandclassificationinvariousapplicationsrangingfromairtrafficcontroltomilitarysurveillance.Targetsizeclassificationisoneoftheimportanttasksinradartargetrecognition,asitprovidesvaluableinformationforunderstandingthenatureandcharacteristicsofdetectedobjects.Inthispaper,weproposearadartargetsizeclassificationmethodbasedonRadarCrossSection(RCS)information.ThemethodutilizesmachinelearningtechniquestoanalyzetheRCSdataofdetectedtargetsandclassifythemintodifferentsizecategories.Experimentalresultsdemonstratetheeffectivenessoftheproposedmethodinaccuratelycategorizingtargetsbasedontheirsize.
1.Introduction:
Withtheincreasingcomplexityanddensityofmodernradarapplications,theabilitytoaccuratelyclassifydifferenttypesandsizesoftargetshasbecomecritical.Sizeclassificationisparticularlyimportantasitenablestheradarsystemtodifferentiatebetweendifferentobjectsandestimatetheirphysicaldimensions.Inthispaper,weproposeamethodthatutilizesRCSinformationtoclassifytargetsbasedontheirsize.
2.RadarCrossSection(RCS):
RadarCrossSection(RCS)isameasureofthereflectivecharacteristicsofatargetinradarsystems.Itprovidesvaluableinformationaboutthesize,shape,andmaterialcompositionofthetarget.RCSinformationisusuallyobtainedthroughanalysisofthereceivedradarsignal.ByanalyzingtheRCSdata,wecanextractmeaningfulfeaturesthatcanbeusedfortargetclassification.
3.FeatureExtraction:
Inordertoclassifytargetsbasedontheirsize,weneedtoextractrelevantfeaturesfromtheRCSdata.Severalfeatureextractiontechniqueshavebeenproposedintheliterature,includingstatisticalfeatures,shapefeatures,andfrequencydomainfeatures.Inourmethod,weemployacombinationofthesefeaturestocapturethesize-relatedcharacteristicsofthetargets.
4.MachineLearningClassification:
Toclassifythetargetsbasedontheirsize,weemploymachinelearningtechniques.Wetrainaclassifierusingalabeleddatasetoftargetswithknownsizes.Theextractedfeaturesareus