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基于稀疏重构的阵列信号波达方向估计算法研究.docx

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基于稀疏重构的阵列信号波达方向估计算法研究 Abstract DirectionofArrival(DOA)estimationisakeytechniqueinarraysignalprocessingandhasbeenwidelyappliedinvariousfields.AmongdifferentDOAestimationtechniques,sparsereconstruction-basedalgorithmshaveachievedgreatsuccessesduetotheirsuperiorperformanceinlowsignal-to-noiseratio(SNR)environmentsandtheabilitytohandlecoherentsources.Inthispaper,wereviewseveralpopularsparsereconstruction-basedalgorithmsforDOAestimation,includingtheFOCUSSalgorithm,theMUSICalgorithm,andthesparseBayesianlearning(SBL)algorithm.WealsoproposeanovelDOAestimationalgorithmbasedonthegroup-sparseBayesianlearning(GSBL)algorithm,whichimprovestherobustnessofSBLalgorithmtothenumberandcoherenceofsourcesbasedonthegroupsparsityinducedbytheuseofmultipleDOAs.Finally,weevaluatetheperformanceofthesealgorithmsusingsimulationexperimentsanddemonstratethesuperiorityoftheproposedalgorithm. I.Introduction DOAestimationreferstofindingoutthearrivaldirectionsofsignalsreceivedbyasensorarrayinthespatialdomain.Ithasbeenwidelyusedinvariousfieldssuchasradar,sonar,wirelesscommunication,andmedicalimaging.ThefundamentalideaofDOAestimationistomakeuseofthespatialdiversityprovidedbyanarraytodifferentiatethesignalsreceivedfromdifferentdirections.Amongdifferentsignalprocessingtechniques,arraysignalprocessinghasbeenwell-developedandhasachievedmanyoutstandingresults. Sparsereconstruction-basedalgorithms,whichidentifytheDOAsbyexploitingthesparsitypropertyofthesignal,haveattractedconsiderableinterestinrecentyears.Thesealgorithmsassumethattheincomingsignalshaveasparserepresentationinsomedomain,andthenestimatetheDOAsbyreconstructingthesparsecoefficients.Sparsereconstruction-basedalgorithmshaveseveraladvantagesoverotherapproaches.Firstly,theycandealwiththeproblemofcoherentsources,whichmeansthatmultiplesourcesarelocatedveryclosetoeachother,leadingtodifficultyinidentifyingthetrueDOAs.Second,theyworkwellinlowSNRenvironmentswheretheconventionalmethodsfailtowork.Finally,theycanhandlemoregeneralsignalmodels,suchasrandomGaussi