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1、Decision-LevelIdentityFusionTanXinLab5,SystemEngineeringDept.Contents1.Introduction2.Classicalinference3.Bayesianinference4.Dempster-Shafer’smethod*5.GeneralizedEvidenceProcessing(GEP)Theory6.Heuristicmethodsforidentityfusion7.Implementationandtrade-offsIntroductionDecision-levelfusionSeek
2、stoprocessidentitydeclarationsfrommultiplesensorstoachieveajointdeclarationofidentity.(Featureextraction,identitydeclaration)Data-levelfusionFeature-levelfusionDecision-levelfusion(Datafused)(jointidentitydeclaration)IntroductionSensorASensorBSensorNFeatureExtractionIdentityDeclarationIden
3、tityDeclarationIdentityDeclarationAssociationDecisionLevelFusion–IdentityFusionIntroductionDecision-LevelFusionTechniquesClassicalinferenceBayesianinferenceDempster-Shafer’smethodGeneralizedevidenceprocessingtheoryHeuristicmethodsClassicalinferenceStatisticalinferencetechniquesseektodrawco
4、nclusionsaboutanunderlyingmechanismordistribution,basedonanobservedsampleofdata.Classicalinferencetypicallyassumesanempiricalprobabilitymodel.Empiricalprobabilityassumesthattheobservedfrequencydistributionwillapproximatetheprobabilityasthenumberoftrials.herentrials,occurrenceofktimesTheore
5、ticalbaseClassicalinferenceOnedisadvantageStrictlyspeaking,empiricalprobabilitiesareonlydefinedforrepeatableevents.Classicalinferencemethodsutilizeempiricalprobabilityandhencearenotstrictlyapplicabletononrepeatableevents,unlesssomemodelcanbedevelopedtocomputetherequisiteprobabilities.Class
6、icalinferenceMaintechnique–hypothesistestingDefinetwohypothesis1.Anullhypothesis,H0(原假設(shè))2.Analternativehypothesis,H1(備擇假設(shè))Testlogic1.Assumethatthenullhypothesis(H0)istrue;2.ExaminetheconsequencesofH0beingtrueinthesamplingdistributionforstatistic;3.Performahypothesistest,iftheobservationhav
7、eahighprobabilityofbeingobservedifH0istrue,thedeclarethedatadonotcontradictH0.4.Otherwise,declarethatthedatatendtocontradictH0.ClassicalinferenceMaintechnique–hypothesistestingTwoassumptionsarerequired1.anexhaustiveandmutuallyexclusivesetofhypothesiscanbedefin
