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复杂电磁环境下的信号盲分离方法 Title:SignalBlindSeparationMethodsinComplexElectromagneticEnvironments Abstract: Inmodernsociety,theelectromagneticenvironmenthasbecomeincreasinglycomplexduetotheproliferationofelectronicdevicesandwirelesscommunicationsystems.Thiscomplexityposessignificantchallengesintheseparationofsignalsfromdifferentsourcesinablindmanner.Variousmethodshavebeenproposedtoaddressthisissue.Thispaperaimstoprovideacomprehensivereviewofdifferentsignalblindseparationmethodsincomplexelectromagneticenvironments,highlightingtheirstrengths,limitations,andpotentialapplications. 1.Introduction Thecontinuousgrowthofwirelesscommunicationsystemsandelectronicdeviceshasledtoanexponentialincreaseintheelectromagneticinterferencewithinoursurroundings.Suchcomplexelectromagneticenvironmentshindertheaccurateandreliabledetectionofdesiredsignals,requiringeffectivetechniquesforsignalblindseparation.Thispaperfocusesonreviewingthefollowingblindseparationmethods:IndependentComponentAnalysis(ICA),SparseComponentAnalysis(SCA),Non-negativeMatrixFactorization(NMF),andCyclostationaryFeatureAnalysis(CFA). 2.IndependentComponentAnalysis(ICA) ICAisawidelyusedsignalseparationtechniquethataimstoextractstatisticallyindependentsourcesfromasetofobservedmixedsignals.Itassumesthattheobservedsignalsarelinearmixturesofindependentcomponentsandexploitstheirstatisticalpropertiestoestimatetheoriginalsignals.ICAhasbeensuccessfullyappliedinmanyapplications,includingspeechrecognition,biomedicalsignalanalysis,andaudiosourceseparation. 3.SparseComponentAnalysis(SCA) SCAisasignalseparationtechniquethatassumesthesourcesaresparseinsomedomain.Byexploitingthesparsityofthesources,SCAaimstorecovertheoriginalsignalsbysolvinganoptimizationproblem.Thismethodhasshownpromisingresultsinscenarioswherethesourcesexhibitsparserepresentations,suchasinimageprocessingandaudiosourceseparation. 4.Non-negativeMatrixFactorization(NMF) NMFisasignalseparationtechniquethatassumesthemixedsignalsarealinearcombinationofnon-negativebasisfunctions.Itde