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最大似然算法在动态DOA估计中的应用研究.docx

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最大似然算法在动态DOA估计中的应用研究 Abstract: DynamicDirectionofArrival(DOA)estimationisusedinvariousfieldsofsignalprocessing.MaximumLikelihood(ML)algorithmisoneofthemostcommonlyusedmethodsforDOAestimationduetoitshighaccuracy.ThispaperdiscussestheapplicationofMLalgorithmindynamicDOAestimationandevaluatesitsperformanceusingsimulation. Introduction: DirectionofArrival(DOA)estimationreferstotheprocessofestimatingtheangleatwhichasignalarrivesatanarrayofsensors.DOAestimationisusedinvariousfields,includingradar,sonar,andwirelesscommunication.DOAestimationcanbedividedintotwocategories:staticDOAestimationanddynamicDOAestimation.StaticDOAestimationiswhenthesignalsourceisassumedtobestationary,andtheDOAisestimatedusingthereceivedsignaldata.DynamicDOAestimationiswhenthesignalsourceisassumedtobemovingandtheDOAisestimatedinreal-time.DynamicDOAestimationismorechallengingthanstaticDOAestimationbecauseoftheneedtotrackthesourcemovement. MaximumLikelihood(ML)algorithmhasbeenwidelyusedforDOAestimationsinceitprovideshighaccuracy.TheMLalgorithmestimatestheDOAbyfindingtheanglethatmaximizesthelikelihoodfunctionofthereceivedsignal.ThelikelihoodfunctionisdefinedastheprobabilitydensityfunctionofthereceivedsignalgiventheDOA.TheMLalgorithmcanalsobeappliedtodynamicDOAestimation,wherethesourcemovementismodeledasastochasticprocess. Inthispaper,wediscusstheapplicationofMLalgorithmindynamicDOAestimation.Thepaperisorganizedasfollows.InSection2,wediscusstheMLalgorithmanditsapplicationinDOAestimation.InSection3,wediscussthedynamicDOAestimationproblemandhowtheMLalgorithmcanbeappliedtoit.InSection4,weevaluatetheperformanceoftheMLalgorithmusingsimulation.InSection5,weconcludethepaper. MaximumLikelihood(ML)AlgorithmandDOAEstimation: TheMLalgorithmisastatisticalmethodthatestimatestheparametersofamodelbymaximizingthelikelihoodfunction.Thelikelihoodfunctionisdefinedastheprobabilitydensityfunctionoftheobserveddatagiventhemodelparameters.InDOAestimation,thelikelihoodfunctionistheprobabilitydensityfunctionofthereceivedsignalda