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ID:39772482
大小:4.17 MB
页数:22页
时间:2019-07-11
《A PMHT approach for extended objects and object groups》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、I.INTRODUCTIONInrealistictrackingapplicationsthereisahighdemandfortherecognitionofextendedobjectsasAPMHTApproachforindividualunits,fortheinitiationofextendedobjecttracks,andforextendedobjecttrackmaintenance.ExtendedObjectsandObjectExtendedobjectsarecharacterizedbyarelativelylargeandoftenstronglyfl
2、uctuatingnumberofsensorGroupsreportsoriginatedbytheindividualscatteringcentersthatarepartofoneandthesameobject.Inthiscontext,weusuallycannotassumethatinsubsequentobjectilluminationsthesamescatteringcenterswillalwaysberesponsibleforthemeasurements.TheMONIKAWIENEKEindividualsensorreportscantherefore
3、nolongerWOLFGANGKOCH,Fellow,IEEEbetreatedinanalogytopointsourcemeasurementsFraunhoferFKIEproducedbyagroupofwell-separatedobjects.In[1]aBayesianapproachtoextendedobjecttrackingusingrandommatricesispresented.WithinConventionaltrackingalgorithmsrelyontheassumptionthisapproach,ellipsoidalobjectextents
4、aremodeledthatthetargetsofinterestarepointsourceobjects.However,byrandommatricesandtreatedasadditionalstateinrealisticscenariosthepointsourceassumptionisoftennotvariablestobeestimated.However,theproposedtrackingmethoddidnotincludeasolutionforsuitableandestimatingtheobjectextentbecomesacrucialaspec
5、t.dataassignmentconflictstypicallyoccurringinRecently,aBayesianapproachtoextendedobjecttrackingusingmulti-objectscenarios.Therefore,wenowpresentrandommatriceshasbeenproposed.Withinthisapproach,themulti-objectextensionofourapproach.Weellipsoidalobjectextensionsaremodeledbyrandommatricesandderiveane
6、wkindofprobabilisticmulti-hypothesistreatedasadditionalstatevariablestobeestimated.However,tracking(PMHT)thatsimultaneouslyestimatestheonlyasingle-objectsolutionhasbeenpresentedsofar.Inthisellipsoidalshapeandthekinematicsofeachobjectworkwepresentthemulti-objectextentofthisapproach.Weusingexpectati
7、on-maximization(EM).Bothellipsoidsderiveanewvariantofprobabilisticmulti-hypothesistrackingandkinematicstatesareiterativelyoptimizedby(PMHT)thatsimultaneouslyestimatestheellipsoidalshapeandspecificKalmanfilterform
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