简介：AspecializedHungarianalgorithmwasdevelopedhereforthemaximumlikelihooddataassociationproblemwithtwoimplementationversionsduetopresenceoffalsealarmsandmisseddetections.Themaximumlikelihooddataassociationproblemisformulatedasabipartiteweightedmatchingproblem.Itsdualityandtheoptimalityconditionsaregiven.TheHungarianalgorithmwithitscomputationalsteps,datastructureandcomputationalcomplexityispresented.Thetwoimplementationversions,Hungarianforest(HF)algorithmandHungariantree(HT)algorithm,andtheircombinationwiththenaveauctioninitializationarediscussed.ThecomputationalresultsshowthatHTalgorithmisslightlyfasterthanHFalgorithmandtheyarebothsuperiortotheclassicMunkresalgorithm.

简介：TheperformanceofthetraditionalVoiceActivityDetection(VAD)algorithmsdeclinessharplyinlowerSignal-to-NoiseRatio(SNR)environments.Inthispaper,afeatureweightinglikeli-hoodmethodisproposedfornoise-robustVAD.Thecontributionofdynamicfeaturestolikelihoodscorecanbeincreasedviathemethod,whichimprovesconsequentlythenoiserobustnessofVAD.Divergencebaseddimensionreductionmethodisproposedforsavingcomputation,whichreducesthesefeaturedimensionswithsmallerdivergencevalueatthecostofdegradingtheperformancealittle.ExperimentalresultsonAuroraIIdatabaseshowthatthedetectionperformanceinnoiseenvironmentscanremarkablybeimprovedbytheproposedmethodwhenthemodeltrainedincleandataisusedtodetectspeechendpoints.Usingweightinglikelihoodonthedimension-reducedfeaturesobtainscom-parable,evenbetter,performancecomparedtooriginalfull-dimensionalfeature.

简介：Duetotheopennessofthecognitiveradionetwork,spectrumsensingdatafalsification(SSDF)canattackthespectrumsensingeasily,whilethereisnoeffectivealgorithmproposedincurrentresearchwork,sothispaperintroducesthemalicioususersremovingtotheweightsequentialprobabilityradiotest(WSPRT).Theterminals'weightisweightedbytheaccuracyoftheirspectrumsensinginformation,whichcanalsobeusedtodetectthemalicioususer.Ifoneterminalownsalowweight,itcanbetreatedasmalicioususer,andshouldberemovedfromtheaggregationcenter.SimulationresultsshowthattheimprovedWSPRTcanachievehigherperformancecomparedwiththeothertwoconventionalsequentialdetectionmethodsunderdifferentnumberofmalicioususers.

简介：Toestimatethespreadingsequenceofthedirectsequencespreadspectrum(DSSS)signal,afastalgorithmbasedonmaximumlikelihoodfunctionisproposed,andthetheoreticalderivationofthealgorithmisprovided.Bysimplifyingtheobjectivefunctionofmaximumlikelihoodestimation,thealgorithmcanrealizesequencesynchronizationandsequenceestimationviaadaptiveiterationandslidingwindow.Sinceitavoidsthecorrelationmatrixcomputation,thealgorithmsignificantlyreducesthestoragerequirementandthecomputationcomplexity.Simulationsshowthatitisafastconvergentalgorithm,andcanperformwellinlowsignaltonoiseratio(SNR).

简介：Blindidentification-blindequalizationforFiniteImpulseResponse(FIR)MultipleInput-MultipleOutput(MIMO)channelscanbereformulatedastheproblemofblindsourcesseparation.Ithasbeenshownthatblindidentificationviadecorrelatingsub-channelsmethodcouldrecovertheinputsources.TheBlindIdentificationviaDecorrelatingSub-channels(BIDS)algorithmfirstconstructsasetofdecorrelators,whichdecorrelatetheoutputsignalsofsubchannels,andthenestimatesthechannelmatrixusingthetransferfunctionsofthedecorrelatorsandfinallyrecoverstheinputsignalusingtheestimatedchannelmatrix.Inthispaper,anewapproximationoftheinputsourceforFIR-MIMOchannelsbasedonthemaximumlikelihoodsourceseparationmethodisproposed.TheproposedmethodoutperformsBIDSinthepresenceofadditivewhiteGaussiannoise.