简介：Thispaperpresentsanoveltimedelayestimation(TDE)methodusingtheconceptofentropy.Therelativedelayisestimatedbyminimizingtheestimatedjointentropyofmultiplesensoroutputsignals.Whenestimatingtheentropy,theinformationaboutthepriordistributionofthesourcesignalisnotrequired.Instead,theParzenwindowestimatorisemployedtoestimatethedensityfunctionofthesourcesignalfrommultiplesensoroutputsignals.Meanwhile,basedontheParzenwindowestimator,theRenyi’squadraticentropy(RQE)isincorporatedtoeffectivelyandefficientlyestimatethehigh-dimensionaljointentropyofthemultichanneloutputs.Furthermore,amodifiedformofthejointentropyforembeddinginformationaboutreverberation(multipathreflections)forspeechsignalsisintroducedtoenhancetheestimator’srobustnessagainstreverberation.

简介：Inthispaper,anewtimingoffsetestimationmethodforOrthogonalFrequencyDivisionMultiplexing(OFDM)systemsarepresented.Uponthereceiptofjustonetrainingsequence,thispresenceofasignalandthestartoftheframecanbedetected.TheperformancesofthetimingestimatorinadditivewhiteGaussiannoiseandthemobilemultipathchannelareevaluatedbysimulation.TheresultsdeliveredbythisalgorithmarecomparedwiththosedeliveredbySchmidlandCox.

简介：一个全球运动评价方法在这份报纸基于柔韧的统计被介绍。由使用追踪的特征点而不是整个图象象素估计参数，这进程加快。为了推进，加快这个过程并且避免数字不稳定性，对这个问题的变质药描述被给，并且这个问题的答案的三种类型被比较。由使用二个步过程，评估者的坚韧性也被改进。自动起始的价值选择是这个方法的一个优点。建议途径被一套例子说明，它以高速度显示出好结果。CLC数字TN911.73

简介：Basedonuniformcirculararrayanditselementoutputsignaldelaydelayanovelmethodforestimationofspatialfrequency,azimuthandelevationispresented.Withoutanyspectralpeaksearchandparameterspairing,theresultingmethodis(?)resolutionandsmallvariance,eveninshortdatalength.Computersimulation(?)effectivenessofthismethod.

简介：AnewDirectionOfArrival(DOA)estimationalgorithmforwidebandsourcesbasedonUniformCircularArray(UCA)ispresentedviaanalyzingwidebandperformanceofthegeneralESPRIT.ThealgorithmeffectivelyimprovesthewidebandperformanceofESPRITbasedontheinterpolationprincipiumandUCA-ESPRIT.Thesimulatedresultsbycomputerdemonstrateitsefficiency.

简介：Inthispaper,thesubspacefittingmodelsfordirection-of-arrival(DOA)estimationisanalyzed,aneffectivealgorithmicapproachisgiven.Astheinitializationvalueissocriticaltotheglobalconvergence,thecontinuationtheoryisalsousedtodevelopanewframeworkwhichsolvestheinitializationproblempowerfully.Somenumericalevidencewillbegiventoshowthattheperformanceofthenewalgorithmisverypromising.

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简介：BasedonthesinglesensorKalmanfilteringequations,thispaperpresentstwo-levelandthree-leveloptimalcentralizedanddistributedestimationalgorithmsforhierarchicalmultisensorsystems.Thesolutionshowsthatwhenthecorrelatedmatrix,themeanofnoise,thecontrolinput,andthemeasurementerrorareallzero,theresultinthispapertumsouttobethestandardalgorithmdiscussed.Simulationshowsthatthemeanofnoise,thecontrolinput,andthemeasurementerrorwillnotchangetheestimationcovarianceandtheestimationcovariancefluctuatesgreatlywhenthecross-correlatedmatrixissimilartothecovarianceofprocessnoise.

简介：TherearetwomajorapproachesforBlindSignalSeparation(BSS)problem:MaximumEntropy(ME)andMinimumMutualInformation(MMI)algorithms.BasedontherecursivearchitectureandtherelationshipbetweentheMEandMMIalgorithms,anExtendedME(EME)algorithmisproposedbyusingprobabilitydensityfunction(pdf)estimationoftheoutputstodeducethecorrespondingiterativeformulasinBSS.Basedonthesimulationresults,itcanbeconcludedthattheproposedalgorithmhasbetterperformancesthanthetraditionalMEalgorithminconvolutemixtureBSSproblems.

简介：ThispaperintroducesamethodforsolvingDOAestimationambiguityinESPRITalgorithmwiththeconventionalbeamformer.Withthehelpofit,foranyspaceoftwosubarrays,thesignalDOAin[-π/2,π/2]canbeestimatedeffectivelybyusingESPRITalgorithm.Finally,somesimulationresultstoverifythetheoreticalanalysesarepresented.

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简介：侦察混血儿雷达信号的一条参数评价途径联合了调音的频率移动(频移键控)，调音的阶段移动(PSK)被介绍。第一，多相的差别被采用计算即时频率(如果)FSK/PSK，那么，频移键控的频率点从直方图被估计如果。PSK的代码率从阶段断绝的地点被提取。最后，收到的信号的平方的多相的差别被计算估计频移键控的代码率。当signal-to-noise比率(SNR)在11dB上面时，介绍算法有参数评价的更高的精确性。

简介：Intheevaluationofsomesimulationsystems,onlysmallsamplesdataaregottenduetothelimitedconditions.Inallusiontotheevaluationproblemofsmallsampledata,anintervalestimationapproachwiththeimprovedgreyconfidencedegreeisproposed.Onthebasisofthedefinitionofgreydistance,threekindsofdefinitionofthegreyweightforeverysampleelementingreyestimatedvalueareputforward,andthentheimprovedgreyconfidencedegreeisdesigned.Inaccordancewiththenewconcept,thegreyintervalestimationforsmallsampledataisdeduced.Furthermore,thebootstrapmethodisappliedformoreaccurategreyconfidenceinterval.Throughresamplingofthebootstrap,numeroussmallsampleswiththecorrespondingconfidenceintervalscanbeobtained.Thenthefinalconfidenceintervaliscalculatedfromtheunionofthesegreyconfidenceintervals.Intheend,thesimulationsystemevaluationusingtheproposedmethodisconducted.Thesimulationresultsshowthatthereasonableconfidenceintervalisacquired,whichdemonstratesthefeasibilityandeffectivenessoftheproposedmethod.

简介：ABayesianestimationmethodtoseparatemulticomponentsignalswithsinglechannelobservationispresentedinthispaper.Byusingthebasisfunctionprojection,thecomponentseparationbecomesaproblemoflimitedparameterestimation.Then,aBayesianmodelforestimatingparametersissetup.ThereversiblejumpMCMC(MonteCarloMarkovChain)algorithmisadoptedtoperformtheBayesiancomputation.Themethodcanjointlyestimatetheparametersofeachcomponentandthecomponentnumber.SimulationresultsdemonstratethatthemethodhaslowSNRthresholdandbetterperformance.

简介：Theproblemofbearingestimationofcoherentsignalimpingiogonanarrayofarbitrarygeometryisstudied.Bothapproachesaredeveloped,thefirstonesynthesizestheobservedarraydataintotheoutputsofalinearuniformarrayandthenprocessesthemviaconventionaltechniquessuchasspatialsmoothingetc.Thesecondapproachisbasedontheinvariantsubspacerotationoperationwhichisequivalenttothetranslationaldisplacementoftheartay,linearlyindependentsignalvectorsareobtainedtherebytospanperfectlythesignalsubspace.Ascomparedwiththeconventionalprocessingtechniques,theapproachbasedoninvariantsubspacerotationdoesnotleadtoaneffectivedecreaseinaperturesizeandthereforeadecreaseinresolutioncapability.Thecomputersimulationsareconductedtodemonstratetheeffectivenessofthepresentedapproaches.

简介：Forthedirectionofarrival(DOA)estimation,traditionalsparsereconstructionmethodsforwidebandsignalsusuallyneedmanyiterationtimes.Forthisproblem,anewmethodfortwo-dimensionalwidebandsignalsbasedonblocksparsereconstructionisproposed.First,aprolatespheroidalwavefunction(PSWF)isusedtofitthewidebandsignals,thentheblocksparsereconstructiontechnologyisemployedforDOAestimation.Theproposedmethodusesorthogonalizationtochoosethematchingatoms,ensuringthattheresidualcomponentscorrespondtotheminimumabsolutevalue.Meanwhile,thevectorsobtainedbyiterationareback-disposedaccordingtothecorrespondingatomicmatchingrules,sotheextraatomsareabandonedinthecourseofiteration,andtheresidualcomponentsofcurrentiterationarereduced.Thustheoriginalsparsesignalsarereconstructed.Theproposedmethodreducesiterationtimescomparingwiththetraditionalreconstructionmethods,andtheestimationprecisionisbetterthantheclassicaltwo-sidedcorrelationtransformation(TCT)algorithmwhenthesnapshotissmallorthesignal-to-noiseratio(SNR)islow.

简介：Compressivesensing(CS)hasemergedasanovelsamplingframeworkwhichenablessparsesignalacquisitionandreconstructionwithfewermeasurementsbelowtheNyquistrate.AnimportantissueforCSistheconstructionofmeasurementmatrixorsensingmatrix.Anewdeterministicsensingmatrix,namedasOOC-B,isproposedbyexploitingopticalorthogonalcodes(OOCs),BernoullimatrixandSingerstructure,whichhastheentriesof0,+1and-1beforenormalization.Wehaveproventhatthedesigneddeterministicmatrixisasymptoticallyoptimal.Inaddition,theproposeddeterministicsensingmatrixisappliedtodirectionofarrival(DOA)estimationofnarrowbandsignalsbyCSarrays(CSA)processingandCSrecovery.TheoreticalanalysisandsimulationresultsshowthattheproposedsensingmatrixhasgoodperformanceforDOAestimation.ItisveryeffectiveforsimplifyinghardwarestructureanddecreasingcomputationalcomplexityinDOAestimationbyCSAprocessing.Besides,lowerrootmeansquareerror(RMSE)andbiasareobtainedinDOAestimationbyCSrecovery.

简介：Alow-complexitymethodfordirectionofarrival(DOA)estimationbasedonestimationsignalparametersviarotationalinvariancetechnique(ESPRIT)isproposed.Insteadofusingthecross-correlationvectorsinmultistageWienerfilter(MSWF),theorthogonalresidualvectorsobtainedinconjugategradient(CG)methodspanthesignalsubspaceusedbyESPRIT.Thecomputationalcomplexityoftheproposedmethodissignificantlyreduced,sincethesignalsubspaceestimationmainlyneedstwomatrixvectorcomplexmultiplicationsattheiterationofdatalevel.Furthermore,thepriortrainingdataarenotneededintheproposedmethod.Toovercomeperformancedegradationatlowsignaltonoiseratio(SNR),theexpandedsignalsubspacespannedbymorebasisvectorsisusedandsimultaneouslyrendersESPRITyieldredundantDOAs,whichcanbeexcludedbyperformingESPRIToncemoreusingtheunexpandedsignalsubspace.ComparedwiththetraditionalESPRITmethodsbyMSWFandeigenvaluedecomposition(EVD),numericalresultsdemonstratethesatisfactoryperformanceoftheproposedmethod.

简介：Inthisletter,capacityestimationforMobileAdhocNETworks(MANETs)usingdirec-tionalantennasarestudied.TwoMatrix-basedFastCalculationAlgorithms(MFCAs)areproposedtoestimatethenetworkcapacityinanetworkscenarioinwhichthereisnochannelsharingamongmultiplesessionsandtrafficissensitivetodelaywithanend-to-enddelayconstraint.Thefirstalgo-rithmMFCA-1isusedtoestimatenetworkcapacityinasituationwherealllinkshavethesamedelay.Itestimatesthemaximumnumberofk-hopsessionsinanetworkbasedonthek-hopadjacencymatrixofthenetwork.ThesecondalgorithmMFCA-2isusedtoestimatenetworkcapacityinasituationwheredifferentlinksmayhavedifferentdelays.Itcalculatesthemaximumnumberofsessionsinanetworkwithanend-to-enddelayconstraintbasedontheadjacencymatrixandthelink-delaymatrixofthenetwork.NumericalandsimulationresultsshowthatbothMFCA-1andMFCA-2cancalculatenetworkcapacitymuchfasterthanthewell-knownBrute-ForceSearchAlgorithm(BFSA)butwiththesameaccuracy.

简介：Thekeyofthesubspace-basedDirectionOfArrival(DOA)estimationliesintheestimationofsignalsubspacewithhighquality.Inthecaseofuncorrelatedsignalswhilethesignalsaretemporallycorrelated,anovelapproachfortheestimationofDOAinunknowncorrelatednoisefieldsisproposedinthispaper.TheapproachisbasedonthebiorthogonalitybetweenamatrixanditsMoore-Penrosepseudoinverse,andmadenoassumptiononthespatialcovariancematrixofthenoise.Theapproachexploitsthestructuralinformationofasetofspatio-temporalcorrelationmatrices,anditcangivearobustandpreciseestimationofsignalsubspace,soapreciseestimationofDOAisobtained.Itsperformancesareconfirmedbycomputersimulationresults.