IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICSPART C APPLICATIONS AND REVIEWS VOL - PDF document

1068IEEETRANSACTIONSONSYSTEMS,MAN,ANDCYBERNETICS—PARTC:APPLICATIONSANDREVIEWS,VOL.37,NO.6,NOVEMBER2007datalinkisprovidedinapositioningsystem,itispossibletosendthemeasurementresultfromaself-positioningmeasuringunittotheremoteside,andthisiscalledindirectremoteposi-tioning,whichisthethirdsystemtopology.Ifthemeasurementresultissentfromaremotepositioningsidetoamobileunitviaawirelessdatalink,thiscaseisnamedindirectself-positioning,whichisthefourthsystemtopology.Ourpaperisdifferentfromtheprevioussurveypapers[1]and[2]inseveralways.Comparingwiththeprevioussurveypaper[1],ourpaperfocusesonindoorapplicationofwirelesslocationpositioningwhile[1]justgenerallydescribesthelo-cationsystemsforubiquitouscomputing,withoutaddressingdifferenttypesoflocationalgorithms,especiallyforwirelesslocationmethods.Also,thepaper[2]presentsaslightout-of-dateoverviewofthetechnologiesforwirelessindoorlocationsolutions,anddoesnotoffermuchdetailaboutthemandper-formancebenchmarkingforindoorwirelesspositioningsystem.Thepublicationdateofthispaperis2002,andsincethen,sev-eralwirelessindoorpositioningsystemsorsolutionshavebeendeveloped.Inthispaper,wepresentthelatestdevelopedsystemsorsolutions,andtheirlocationalgorithms.Ourmainpurposeistoprovideaqualitativeoverviewforthem.Whenpossible,wealsoofferaquantitivecomparisonofthesesystemsorsolutions.Thisreviewpaperisorganizedasfollows.SectionIIshowsthemeasuringprinciplesforlocationsensingandtheposition-ingalgorithmscorrespondingtodifferentmeasuringprinciples.Performancemetricsforindoorpositioningtechniquesareex-plainedinSectionIII.SectionIVpresentscurrentwirelessin-doorpositioningsystemsandsolutions,andtheirperformancecomparison.Finally,SectionVconcludesthepaperandgivespossiblefuturedirectionsforresearchonwirelesspositioningsystemsforindoorenvironments.II.MRINCIPLESANDItisnoteasytomodeltheradiopropagationintheindoorenvironmentbecauseofseveremultipath,lowprobabilityforavailabilityofline-of-sight(LOS)path,andspecicsiteparam-eterssuchasoorlayout,movingobjects,andnumerousreect-ingsurfaces.Thereisnogoodmodelforindoorradiomultipathcharacteristicsofar[2].Exceptusingtraditionaltriangulation,positioningalgorithmsusingsceneanalysisorproximityaredevelopedtomitigatethemeasurementerrors.Targetingdiffer-entapplicationsorservices,thesethreealgorithmshaveuniqueadvantagesanddisadvantages.Hence,usingmorethanonetypeofpositioningalgorithmsatthesametimecouldgetbetterA.TriangulationTriangulationusesthegeometricpropertiesoftrianglestoestimatethetargetlocation.Ithastwoderivations:laterationandangulation.Laterationestimatesthepositionofanobjectbymeasuringitsdistancesfrommultiplereferencepoints.So,itisalsocalledrangemeasurementtechniques.Insteadofmeasur-ingthedistancedirectlyusingreceivedsignalstrengths(RSS),timeofarrival(TOA)ortimedifferenceofarrival(TDOA)isusuallymeasured,andthedistanceisderivedbycomputingthe Fig.1.PositioningbasedonTOA/RTOFmeasurements.attenuationoftheemittedsignalstrengthorbymultiplyingtheradiosignalvelocityandthetraveltime.Roundtriptimeofight(RTOF)orreceivedsignalphasemethodisalsousedforrangeestimationinsomesystems.Angulationlocatesanobjectbycomputinganglesrelativetomultiplereferencepoints.Inthissurvey,wefocusontheaforementionedmeasurementsintheshorterrange,low-antenna,andindoorenvironment.1)LaterationTechniques:a)TOA:Thedistancefromthemobiletargettothemea-suringunitisdirectlyproportionaltothepropagationtime.Inordertoenable2-Dpositioning,TOAmeasurementsmustbemadewithrespecttosignalsfromatleastthreereferencepoints,asshowninFig.1[4].ForTOA-basedsystems,theone-waypropagationtimeismeasured,andthedistancebetweenmea-suringunitandsignaltransmitteriscalculated.Ingeneral,directTOAresultsintwoproblems.First,alltransmittersandreceiversinthesystemhavetobepreciselysynchronized.Second,atimes-tampmustbelabeledinthetransmittingsignalinorderforthemeasuringunittodiscernthedistancethesignalhastraveled.TOAcanbemeasuredusingdifferentsignalingtechniquessuchasdirectsequencespread-spectrum(DSSS)[22],[23]orultra-wideband(UWB)measurements[78].Astraightforwardapproachusesageometricmethodtocom-putetheintersectionpointsofthecirclesofTOA.Thepositionofthetargetcanalsobecomputedbyminimizingthesumofsquaresofanonlinearcostfunction,i.e.,least-squaresalgo-rithm[4],[5].Itassumesthatthemobileterminal,locatedat),transmitsasignalattime,thebasestationslo-catedat(),()receivethesignalattime.Asaperformancemeasure,thecostfunctioncanbeformedbycanbechosentoreectthereliabilityofthesignalreceivedatthemeasuringunit,andisgivenasfollows. isthespeedoflight,andx,y,t.Thisfunctionisformedforeachmeasuringunit,,...,N,bemadezerowiththeproperchoiceofx,y,and.ThelocationestimateisdeterminedbyminimizingthefunctionThereareotheralgorithmsforTOA-basedindoorlocationsystemsuchasclosest-neighbor(CN)andresidualweighting(RWGH)[5].TheCNalgorithmestimatesthelocationofthe Authorized licensed use limited to: University of Pittsburgh. Downloaded on January 27, 2009 at 17:04 from IEEE Xplore. Restrictions apply. 1070IEEETRANSACTIONSONSYSTEMS,MAN,ANDCYBERNETICS—PARTC:APPLICATIONSANDREVIEWS,VOL.37,NO.6,NOVEMBER2007 Fig.4.Positioningbasedonsignalphase. Fig.5.PositioningbasedonAOAmeasurement.beignoredifitissmall,comparedwiththetransmissiontime.However,forshort-rangesystems,itcannotbeignored.Analter-nativeapproachistousetheconceptofmodulatedreection[9],whichisonlysuitedforshort-rangesystems.AnalgorithmtomeasureRTOFofwirelessLANpacketsispresentedin[10]withtheresultofameasurementerrorofafewmeters.ThepositioningalgorithmsforTOAcanbedirectlyapplicableforRTOF.e)ReceivedSignalPhaseMethod:Thereceivedsignalphasemethodusesthecarrierphase(orphasedifference)toestimatetherange.Thismethodisalsocalledphaseofarrival(POA)[2].Assumingthatalltransmittingstationsemitpuresinusoidalsignalsthatareofthesamefrequency,withzerophaseoffset,inordertodeterminethephasesofsignalsre-ceivedatatargetpoint,thesignaltransmittedfromeachtrans-mittertothereceiverneedsanitetransitdelay.InFig.4,thetransmitterstationsAuptoDareplacedatparticularlocationswithinanimaginarycubicbuilding.Thedelayisexpressedasafractionofthesignal’swavelength,andisdenotedwiththe=(2fDinequation)=sin(2ftA,B,C,Disthespeedoflight.Aslongasthetransmittedsignal’swavelengthislongerthanthedi-agonalofthecubicbuilding,i.e.,,wecangettherangeestimation.Then,wecanusethesamepositioningalgorithmsusingTOAmeasurement.Thereceivermaymeasurephasedifferencesbetweentwosignalstransmit-tedbypairsofstations,andpositioningsystemsareabletoadoptthealgorithmsusingTDOAmeasurementtolocatethetarget.Foranindoorpositioningsystem,itispossibletousethesignalphasemethodtogetherwithTOA/TDOAorRSSmethodtone-tunethelocationpositioning.However,there-ceivedsignalphasemethodhasoneproblemofambiguouscar-rierphasemeasurementstoovercome.ItneedsanLOSsig-nalpath,otherwiseitwillcausemoreerrorsfortheindoorenvironment.2)AngulationTechniques(AOAEstimation):InAOA,thelocationofthedesiredtargetcanbefoundbytheintersectionofseveralpairsofangledirectionlines,eachformedbythecircularradiusfromabasestationorabeaconstationtothemobiletarget.AsshowninFig.5,AOAmethodsmayuseatleasttwoknownreferencepoints(A,B),andtwomeasuredanglestoderivethe2-Dlocationofthetarget.EstimationofAOA,commonlyreferredtoasdirectionnding(DF),canbeaccomplishedeitherwithdirectionalantennaeorwithanarrayofantennae.TheadvantagesofAOAarethatapositionestimatemaybedeterminedwithasfewasthreemeasuringunitsfor3-Dpo-sitioningortwomeasuringunitsfor2-Dpositioning,andthatnotimesynchronizationbetweenmeasuringunitsisrequired.Thedisadvantagesincluderelativelylargeandcomplexhard-warerequirement(s),andlocationestimatedegradationasthemobiletargetmovesfartherfromthemeasuringunits.Forac-curatepositioning,theanglemeasurementsneedtobeaccurate,butthehighaccuracymeasurementsinwirelessnetworksmaybelimitedbyshadowing,bymultipathreectionsarrivingfrommisleadingdirections,orbythedirectivityofthemeasuringaperture.SomeliteraturesalsocallAOAasdirectionofarrival(DOA).FormoredetaileddiscussionsonAOAestimationalgo-rithmsandtheirproperties,see[11]–[13].B.SceneAnalysisRF-basedsceneanalysisreferstothetypeofalgorithmsthatrstcollectfeatures(ngerprints)ofasceneandthenestimatethelocationofanobjectbymatchingonlinemeasurementswiththeclosestapriorilocationngerprints.RSS-basedlocationngerprintingiscommonlyusedinsceneanalysis.LocationngerprintingreferstotechniquesthatmatchtheÞngerprintofsomecharacteristicofasignalthatislocationdependent.Therearetwostagesforlocationngerprinting:ofinestageandonlinestage(orrun-timestage).Duringtheofinestage,asitesurveyisperformedinanenvironment.Thelocationcoordinates/labelsandrespectivesignalstrengthsfromnearbybasestations/measuringunitsarecollected.Duringtheonlinestage,alocationpositioningtechniqueusesthecurrentlyobservedsignalstrengthsandpreviouslycollectedinformationtogureoutanestimatedlocation.Themainchallengetothetechniquesbasedonlocationngerprintingisthatthereceivedsignalstrengthcouldbeaffectedbydiffraction,reection,andscatteringinthepropagationindoorenvironments.Thereareatleastvelocationngerprinting-basedposition-ingalgorithmsusingpatternrecognitiontechniquesofar:prob-abilisticmethods,-nearest-neighbor(NN),neuralnetworks,supportvectormachine(SVM),andsmallestM-vertexpolygon1)ProbabilisticMethods:Onemethodconsidersposition-ingasaclassicationproblem.Assumingthattherearetioncandidates,...,L,andistheobservedsignalstrengthvectorduringtheonlinestage,thefollowingcanbeobtained:i,j,...,n,j etal.:SURVEYOFWIRELESSINDOORPOSITIONINGTECHNIQUESANDSYSTEMSdenotestheprobabilitythatthemobilenodeisinlocation,giventhatthereceivedsignalvectoris.Alsoassumethat)istheprobabilitythatthemobilenodeisinlocation.Thegivendecisionruleisbasedonprobability.UsingBayes’formula,andassumingthati,j,...,nwehavethefollowingdecisionrulebasedonthelikelihoodthat()istheprobabilitythatthesignalvectorisreceived,giventhatthemobilenodeislocatedinlocationi,j,...,n,jInadditiontothehistogramapproach,kernelapproachisusedincalculatinglikelihood.AssumingthatthelikelihoodofeachlocationcandidateisaGaussiandistribution,themeanandstandarddeviationofeachlocationcandidatecanbecalculated.Ifthemeasuringunitsintheenvironmentareindependent,wecancalculatetheoveralllikelihoodofonelocationcandidatebydirectlymultiplyingthelikelihoodsofallmeasuringunits.Therefore,thelikelihoodofeachlocationcandidatecanbecal-culatedfromobservedsignalstrengthsduringtheonlinestage,andtheestimatedlocationistobedecidedbythepreviousdeci-sionrule.However,thisisapplicableonlyfordiscretelocationcandidates.Mobileunitscouldbelocatedatanyposition,notjustatthediscretepoints.Theestimated2-Dlocationgivenby(5)mayinterpolatethepositioncoordinatesandgivemoreaccurateresults.Itisaweightedaverageofthecoordinatesofallsamplinglocationsy)=Otherprobabilisticmodelingtechniquesforlocation-awareandlocation-sensitiveapplicationsinwirelessnetworksmayinvolvepragmaticallyimportantissueslikecalibration,ac-tivelearning,errorestimation,andtrackingwithhistory.SoBayesian-network-basedand/ortracking-assistedpositioninghasbeenproposed[48].NNaveragingusestheonlineRSStosearchclosestmatchesofknownlocationsinsignalspacefromthepreviously-builtdatabaseaccordingtorootmeansquareerrorsprinciple.Byaveragingtheselocationcandidateswithorwithoutadoptingthedistancesinsignalspaceasweights,anestimatedlocationisobtainedviaweightedNNorunweightedNN.Inthisapproach,istheparameteradaptedforbetter3)NeuralNetworks:Duringtheofinestage,RSSandthecorrespondinglocationcoordinatesareadoptedastheinputsandthetargetsforthetrainingpurpose.Aftertrainingofneuralnetworks,appropriateweightsareobtained.Usually,amulti-layerperceptron(MLP)networkwithonehiddenlayerisusedforneural-networks-basedpositioningsystem.Theinputvectorofsignalstrengthsismultipliedbythetrainedinputweightma-trix,andthenaddedwithinputlayerbiasifbiasischosen.Theresultisputintothetransferfunctionofthehiddenlayerneuron.Theoutputofthistransferfunctionismultipliedbythetrainedhiddenlayerweightmatrix,andthenaddedtothehiddenlayerbiasifitischosen.Theoutputofthesystemisatwo-elementvectororathree-elementsvector,whichmeansthe2-Dor3-Doftheestimatedlocation.4)SVM:SVMisanewandpromisingtechniquefordataclassicationandregression.Itisatoolforstatisticalanalysisandmachinelearning,anditperformsverywellinmanyclassi-cationandregressionapplications.SVMshavebeenusedexten-sivelyforawiderangeofapplicationsinscience,medicine,andengineeringwithexcellentempiricalperformance[15],[16].ThetheoryofSVMisfoundin[17]and[18].Supportvec-torclassication(SVC)ofmultipleclassesandsupportvectorregression(SVR)havebeenusedsuccessfullyinlocationn-gerprinting[19],[20].5)SMP:SMPusestheonlineRSSvaluestosearchforcan-didatelocationsinsignalspacewithrespecttoeachsignaltrans-mitterseparately.M-vertexpolygonsareformedbychoosingatleastonecandidatefromeachtransmitter(supposetotalofMtransmitters).Averagingthecoordinatesofverticesofthesmall-estpolygon(whichhastheshortestperimeter)givesthelocationestimate.SMPhasbeenusedinMultiLoc[74].C.ProximityProximityalgorithmsprovidesymbolicrelativelocationin-formation.Usually,itreliesuponadensegridofantennas,eachhavingawell-knownposition.Whenamobiletargetisde-tectedbyasingleantenna,itisconsideredtobecollocatedwithit.Whenmorethanoneantennadetectsthemobiletarget,itisconsideredtobecollocatedwiththeonethatreceivesthestrongestsignal.Thismethodisrelativelysimpletoimplement.Itcanbeimplementedoverdifferenttypesofphysicalmedia.Inparticular,thesystemsusinginfraredradiation(IR)andradiofrequencyidentication(RFID)areoftenbasedonthismethod.Anotherexampleisthecellidentication(Cell-ID)orcelloforigin(COO)method.Thismethodreliesonthefactthatmo-bilecellularnetworkscanidentifytheapproximatepositionofamobilehandsetbyknowingwhichcellsitethedeviceisusingatagiventime.ThemainbenetofCell-IDisthatitisalreadyinusetodayandcanbesupportedbyallmobilehandsets.III.PItisnotenoughtomeasuretheperformanceofapositioningtechniqueonlybyobservingitsaccuracy.Referringto[21]andconsideringthedifferencebetweentheindoorandoutdoorwire-lessgeolocation,weprovidethefollowingperformancebench-markingforindoorwirelesslocationsystem:accuracy,preci-sion,complexity,scalability,robustness,andcost.Thereafter,wemakeacomparisonamongdifferentsystemsandsolutionsinSectionIV.A.AccuracyAccuracy(orlocationerror)isthemostimportantrequire-mentofpositioningsystems.Usually,meandistanceerrorisadoptedastheperformancemetric,whichistheaverageEuclideandistancebetweentheestimatedlocationandthetruelocation.Accuracycanbeconsideredtobeapotentialbias,or 1072IEEETRANSACTIONSONSYSTEMS,MAN,ANDCYBERNETICS—PARTC:APPLICATIONSANDREVIEWS,VOL.37,NO.6,NOVEMBER2007systematiceffect/offsetofapositioningsystem.Thehighertheaccuracy,thebetterthesystem;however,thereisoftenatradeoffbetweenaccuracyandothercharacteristics.Somecompromisebetween“suitable”accuracyandothercharacteristicsisneeded.B.PrecisionAccuracyonlyconsidersthevalueofmeandistanceerrors.However,locationprecisionconsidershowconsistentlythesys-temworks,i.e.,itisameasureoftherobustnessoftheposi-tioningtechniqueasitrevealsthevariationinitsperformanceovermanytrials.Wealsonoticethatsomeliteraturesdenethelocationprecisionasthestandarddeviationinthelocationerrororthegeometricdilutionofprecision(GDOP),butwepreferitasthedistributionofdistanceerrorbetweentheestimatedlocationandthetruelocation.Usually,thecumulativeprobabilityfunctions(CDF)ofthedistanceerrorisusedformeasuringtheprecisionofasystem.Whentwopositioningtechniquesarecompared,iftheiraccu-raciesarethesame,wepreferthesystemwiththeCDFgraph,whichreacheshighprobabilityvaluesfaster,becauseitsdis-tanceerrorisconcentratedinsmallvalues.Inpractice,CDFisdescribedbythepercentileformat.Forexample,onesystemhasalocationprecisionof90%within2.3m(theCDFofdistanceerrorof2.3mis0.9),and95%within3.5m;anotheronehasaprecisionof50%within2.3mand95%within3.3m.Wecouldchoosetheformersystembecauseofitshigherprecision.C.ComplexityComplexityofapositioningsystemcanbeattributedtohard-ware,software,andoperationfactors.Inthispaper,weem-phasizeonsoftwarecomplexity,i.e.,computingcomplexityofthepositioningalgorithm.Ifthecomputationofthepositioningalgorithmisperformedonacentralizedserverside,theposition-ingcouldbecalculatedquicklyduetothepowerfulprocessingcapabilityandthesufcientpowersupply.Ifitiscarriedoutonthemobileunitside,theeffectsofcomplexitycouldbeevident.Mostofthemobileunitslackstrongprocessingpowerandlongbatterylife;so,wewouldpreferpositioningalgorithmswithlowcomplexity.Usually,itisdifculttoderivetheanalyticcomplexityformulaofdifferentpositioningtechniques;thus,thecomputingtimeisconsidered.Locationrateisanimportantindicatorforcomplexity.Thedualoflocationrateislocationlag,whichisthedelaybetweenamobiletargetmovingtoanewlocationandreportingthenewlocationofthattargetbytheD.RobustnessApositioningtechniquewithhighrobustnesscouldfunctionnormallyevenwhensomesignalsarenotavailable,orwhensomeoftheRSSvalueoranglecharacterareneverseenbefore.Sometimes,thesignalfromatransmitterunitistotallyblocked,sothesignalcannotbeobtainedfromsomemeasuringunits.Theonlyinformationtoestimatethelocationisthesignalfromothermeasuringunits.Sometimes,somemeasuringunitscouldbeoutoffunctionordamagedinaharshenvironment.Thepositioningtechniqueshavetousethisincompleteinformationtocomputethelocation.E.ScalabilityThescalabilitycharacterofasystemensuresthenormalpo-sitioningfunctionwhenthepositioningscopegetslarge.Usu-ally,thepositioningperformancedegradeswhenthedistancebetweenthetransmitterandreceiverincreases.Alocationsys-temmayneedtoscaleontwoaxes:geographyanddensity.Geographicscalemeansthattheareaorvolumeiscovered.Densitymeansthenumberofunitslocatedperunitgeographicarea/spacepertimeperiod.Asmorearea/spaceiscoveredorunitsarecrowdedinanarea/space,wirelesssignalchannelsmaybecomecongested,morecalculationmaybeneededtoperformlocationpositioning,ormorecommunicationinfras-tructuremayberequired.Anothermeasureofscalabilityisthedimensionalspaceofthesystem.Thecurrentsystemcanlocatetheobjectsin2-Dor3-Dspace.Somesystemscansupportboth2-Dand3-Dspaces.F.CostThecostofapositioningsystemmaydependonmanyfactors.Importantfactorsincludemoney,time,space,weight,anden-ergy.Thetimefactorisrelatedtoinstallationandmaintenance.Mobileunitsmayhavetightspaceandweightconstraints.Mea-suringunitdensityisconsideredtobeaspacecost.Sometimes,wehavetoconsidersomesunkcosts.Forexample,aposition-ingsystemlayeredoverawirelessnetworkmaybeconsideredtohavenohardwarecostifallthenecessaryunitsofthatnet-workhavealreadybeenpurchasedforotherpurposes.Energyisanimportantcostfactorofasystem.Somemobileunits(e.g.,electronicarticlesurveillance(EAS)tagsandpassiveRFIDtags,whichareaddressedlater)arecompletelyenergypassive.Theseunitsonlyrespondtoexternaleldsand,thus,couldhaveanunlimitedlifetime.Othermobileunits(e.g.,deviceswithrechargeablebattery)havealifetimeofseveralhourswithoutrecharging.IV.SURVEYOFYSTEMSANDHavingidentiedthecommonmeasuringprinciples,thepo-sitioningalgorithmsandtheimportantperformancemetricsoflocationpositioningsystems,weareabletodiscussspecicsys-tems.Therearetwobasicapproachestodesigningawirelessgeolocationsystem.Therstapproachistodevelopasignal-ingsystemandanetworkinfrastructureoflocationmeasuringunitsfocusedprimarilyonwirelesslocationapplication.Thesecondapproachistouseanexistingwirelessnetworkinfras-tructuretolocateatarget.Theadvantageoftherstapproachisthatthedesignersareabletocontrolphysicalspecicationand,consequently,thequalityofthelocationsensingresults.Thetagwiththetargetcanbedesignedasaverysmallwearabletagorsticker,andthedensityofthesensorcanbeadjustedtotherequiredpositioningaccuracy.Theadvantageofthesecondapproachisthatitavoidsexpensiveandtime-consumingde-ploymentofinfrastructure.Thesesystems,however,mayneed etal.:SURVEYOFWIRELESSINDOORPOSITIONINGTECHNIQUESANDSYSTEMS Fig.6.Outlineofcurrentwireless-basedpositioningsystems.tousemoreintelligentalgorithmstocompensateforthelowaccuracyofthemeasuredmetrics.Severaltypesofwirelesstechnologiesareusedforindoorlocation.Fig.6depictsaroughoutlineofthecurrentwireless-basedpositioningsystems,whichisamodiedversionof[24,Fig.2].Itisbeyondthescopeofthispapertoprovideacompleteoverviewofsystemsavailabletillnow.Wefocusonthewirelesspositioningsystemsprimarilyforindoorsituations.Therearesomeclassicationapproachestosurveyingtheindoorpositioningsystem,suchasapplicationen-vironments(suchas2-D/3-Dpositioninginofce,warehouse,etc.),positioningalgorithms,andwirelesstechnologies.Inthispaper,weadoptthewirelesstechnologiesscheme,alsoaddress-ingtheirpositioningalgorithmsandtheirapplicationsituation.A.GPS-BasedGlobalpositioningsystem(GPS),oritsdifferentialcomple-mentDGPS[25],isoneofthemostsuccessfulpositioningsystemsinoutdoorenvironments.However,poorcoverageofsatellitesignalforindoorenvironmentsdecreasesitsaccuracyandmakesitunsuitableforindoorlocationestimation.SnapTrack,aQualcommCompany,pioneeredwirelessas-sistedGPS(A-GPS)toovercomethelimitationsofconventionalGPS,andprovideGPSindoorstechniquewithanaverageof5–50maccuracyinmostindoorenvironments.A-GPStechnol-ogyusesalocationserverwithareferenceGPSreceiverthatcansimultaneouslydetectthesamesatellitesasthewirelesshandset(ormobilestation)withapartialGPSreceiver,tohelpthepar-tialGPSreceiverndweakGPSsignals.ThewirelesshandsetcollectsmeasurementsfromboththeGPSconstellationandthewirelessmobilenetwork.Thesemeasurementsarecombinedbythelocationservertoproduceapositionestimation.Recently,AtmelandU-bloxannouncedtheavailabilityofanewGPSweaksignaltrackingtechnology,calledSuperSense.WiththisnewGPSsoftware,GPSnavigationbecomespossibleinbuildinginteriorsanddeepurbancanyonsbecauseofitsSnapTrack.http://www.snaptrack.com/AtmelCorporation.http://www.atmel.com/U-bloxAG.http://www.u-blox.comtrackingsensitivitybeyond158dBm.Itsperformanceisnotreportedsofar.LocataCorporationhasinventedanewpositioningtech-nologycalled[26],forprecisionpositioningbothin-doorsandoutside.Partofthe“Locatatechnology”consistsofatime-synchronizedpseudolitetransceivercalledaLocataLite.AnetworkofLocataLitesformsaLocataNet,whichtransmitsGPS-likesignalsthatallowsingle-pointpositioningusingcarrier-phasemeasurementsforamobiledevice(a).TheSatelliteNavigationAndPositioning(SNAP)GroupattheUni-versityofNewSouthWaleshasassistedinthedevelopmentofaandtestingofthenewtechnology.Thetestexperimentsdemonstrateproof-of-conceptforthe“Locatatechnology,”andshowthatcarrier-phasepointpositioning(withoutradiomodemdatalinks)ispossiblewithsubcentimeterprecision[26].B.RFIDRFIDisameansofstoringandretrievingdatathroughelec-tromagnetictransmissiontoanRFcompatibleintegratedcircuitandisnowbeingseenasameansofenhancingdatahandlingprocesses[27].AnRFIDsystemhasseveralbasiccomponents,includinganumberofRFIDreaders,RFIDtags,andthecom-municationbetweenthem.TheRFIDreaderisabletoreadthedataemittedfromRFIDtags.RFIDreadersandtagsuseade-nedRFandprotocoltotransmitandreceivedata.RFIDtagsarecategorizedaseitherpassiveoractive.PassiveRFIDtagsoperatewithoutabattery.Theyaremainlyusedtoreplacethetraditionalbarcodetechnologyandaremuchlighter,smallerinvolume,andlessexpensivethanactivetags.TheyreecttheRFsignaltransmittedtothemfromareaderandaddinformationbymodulatingthereectedsignal.However,theirrangesareverylimited.Thetypicalreadingrangeis1–2m,andthecostofthereadersisrelativelyhigh.PassiveRFIDsys-temsusuallymakeuseoffourfrequencybands:LF(125kHz),HF(13.56MHz),UHF(433,868–915MHz),andmicrowavefrequency(2.45GHz,5.8GHz).BewatorisaknownpassiveRFIDmanufacturer.ActiveRFIDtagsaresmalltransceivers,whichcanactivelytransmittheirID(orotheradditionaldata)inreplytoaninterro-gation.FrequencyrangesusedaresimilartothepassiveRFIDcaseexceptthelow-frequencyandhigh-frequencyranges.TheadvantagesofactiveRFIDarewiththesmallerantennaeandinthemuchlongerrange(canbetensofmeters).Activetagsareideallysuitedfortheidenticationofhigh-unit-valueproductsmovingthroughaharshassemblyprocess.WaveTrendTech-isoneofthefamousActiveRFIDmanufacturers.Awell-knownlocationsensingsystemusingtheRFIDtechnol-ogyisSpotON[28].SpotONusesanaggregationalgorithmfor3-Dlocationsensingbasedonradiosignalstrengthanalysis.SpotONresearchersdesignedandbuilthardwarethatservesasobjectlocationtags.IntheSpotONapproach,objectsarelocatedbyhomogenoussensornodeswithoutcentralcontrol,i.e.,AdHocmanner.SpotONtagsusereceivedRSSvalueasAtmel/U-blox.http://www.automotivedesignline.com/products/164901239BewatorLtd.http://www.bewator.com/uk/WaveTrendTechnologiesLtd.http://www.wavetrend.co.za/ 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1078IEEETRANSACTIONSONSYSTEMS,MAN,ANDCYBERNETICS—PARTC:APPLICATIONSANDREVIEWS,VOL.37,NO.6,NOVEMBER2007wirelesssensornetworksinawidevarietyofapplications,includingindoorlocationpositioning[65].Suchsystemsusingwirelesssensornetworkhavebeendescribedas“cooperative,”“relative,”“multi-hop,”“GPS-free,”or“network”localization;“ad-hoc”or“sensor”positioning;and“self-localization”invariouspapers.Communicationandmeasurementsbetweenmanypairsofsensorsarerequiredtoachievelocalizationforallsensors.Wereferthereadersto[14]formoredetailsaboutcooperativelocalization.Uptonow,twomajorsensornetworkstandardsaretheIEEE802.15.4physical(PHY)layerandmediumaccesscontrol(MAC)layerstandardforlow-ratewirelesspersonal-areanetworks(LR-WPANs),andtheZigBeenetworkingandapplicationlayerstandard[67].Thesestandardsallowforlocalizationinformationtobemeasuredbetweenpairsofsensors.Inparticular,RSScanbemeasuredinthe802.15.4PHYstandardviathelinkqualityindication(LQI),whichreportsthesignalstrengthassociatedwithareceivedpackettohigherlayers.Mostofthesensor-network-basedlocationestimationsuseRSSmeasurement[68],[69].SomesystemsalsouseTOAmeasurement[68],[70].OtherstakeAOAmeasurementsuchasadhocpositioningsystem(APS)[71].TableIbrieycomparesthecurrentsystemsandsolutions.Thesystemssolutionsshowninthistablearemainlytheoneswhosespecicationshavebeenreportedbytheirdevelopers.Wehaveexcludedthecasesinwhichlittleornoinformationonthemhasbeenmadeavailable.V.CONCLUSIONANDThispapersurveysthecurrentindoorpositioningtechniquesandsystems.Differentperformancemeasurementcriteriaarediscussedandseveraltradeoffsamongthemareobserved.Forexample,theonebetweencomplexityandaccuracy/precisionneedscarefulconsiderationwhenwechoosepositioningsys-temsandtechniquesfordifferentapplicationsenvironmentssuchaswarehousing,robotics,oremergency.Usually,loca-tionngerprintingschemeisbetterforopenareaswhileActiveRFIDissuitablefordenseenvironments.Intermsofscalabilityandavailability,thesepositioningtechniquesandsystemshavetheirownimportantcharacteristicswhenappliedinrealenvi-ronments.Thechoiceoftechniqueandtechnologysignicantlyaffectsthegranularityandaccuracyofthelocationinformation.Futuretrendsofwirelessindoorpositioningsystemsareasfollows.1)Neworhybridpositionalgorithmsareneeded.Afewoftheworkshavealreadybeenstartedsupportingsuchalgo-rithms.Forexample,acalibration-freelocationalgorithmbasedontriangulation,triangularinterpolationandextrap-olation(TIX),isintroducedin[75].Ahybridalgorithmispresentedin[76]forindoorpositioningusingWLANthataimstocombinethebenetsoftheRFpropagationlossmodelandngerprintingmethod.Thesameworkhasbeendonein[77].Theselectivefusionlocationestima-tion(SELFLOC)[72]algorithminferstheuserlocationbyselectivelyfusinglocationinformationfrommultiplewirelesstechnologiesand/ormultipleclassicallocationalgorithmsinatheoreticallyoptimalmanner.2)Internetworkingofdifferentwirelesspositioningsystemsisaresearchandpracticaltopicinordertoextendthepositioningrange.3)Wirelesscombinedwithothertechnologiessuchasoptical(e.g.,IR),inertial,dcelectromagneticandultrasonicforindoorlocationisanothertrend.Howtocombinethesetechnologiesintoapracticalsystemisatopicofsensor4)Howtodeploysensorstoimprovethepositioningaccu-racy,howtonishdeployingwirelesspositioningsysteminashorttime,especiallyforemergencyresponderappli-cationisalsoworthconsidering[73].5)WirelessindoorlocationusingUWB(from3.1to10.6GHz)techniquesandindoorpositioningusingmo-bilecellularnetworkareotherpromisingresearchtop-ics[31].6)Howtointegrateindoorandoutdoorpositioningsystemisanotherareaofresearch.Thisintegrationmayhelpindevelopingmoreefcientandrobustdetectionsystemsforpositioningofmobilecomputingnodes.Inthiscase,amobilenodewillbetrackedindoororoutdoorusingthesamedetectionsystem.[1]J.HightowerandG.Borriello,“Locationsystemsforubiquitouscomput-,vol.34,no.8,Aug.2001.[2]K.Pahlavan,X.Li,andJ.Makela,“Indoorgeolocationscienceandtech-nology,”IEEECommun.Mag.,vol.40,no.2,pp.112–118,Feb.2002.[3]C.Drane,M.Macnaughtan,andC.Scott,“PositioningGSMtelephones,”IEEECommun.Mag.,vol.36,no.4,pp.46–54,59,Apr.1998.[4]B.Fang,“Simplesolutionforhyperbolicandrelatedpositionxes,”Trans.Aerosp.Electron.Syst.,vol.26,no.5,pp.748–753,Sep.1990.[5]M.KanaanandK.Pahlavan,“Acomparisonofwirelessgeolocational-gorithmsintheindoorenvironment,”inProc.IEEEWirelessCommun.Netw.Conf.,2004,vol.1,pp.177–182.[6]D.Torrieri,“Statisticaltheoryofpassivelocationsystems,”IEEETrans.Aerosp.Electron.Syst.,vol.20,no.2,pp.183–197,Mar.1984.[7]J.Zhou,K.M.-K.Chu,andJ.K.-Y.Ng,“Providinglocationserviceswithinaradiocellularnetworkusingellipsepropagationmodel,”inProc.19thInt.Conf.Adv.Inf.Netw.Appl.,Mar.2005,pp.559–564.[8]A.TeuberandB.Eissfeller,“Atwo-stagefuzzylogicapproachforwirelessLANindoorpositioning,”inProc.IEEE/IONPositionLocationNavigat.,Apr.2006,vol.4,pp.730–738.[9]M.Kossel,H.R.Benedickter,R.Peter,andW.Bachtold,“Microwavebackscattermodulationsystems,”IEEEMTT-SDig.,vol.3,pp.1427–1430,Jun.2000.[10]A.GuntherandC.Hoene,“MeasuringroundtriptimestodeterminethedistancebetweenWLANnodes,”inProc.Netw.2005.,Waterloo,ON,Ca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HuiLiu(S’)receivedtheB.S.degreeinelectri-calengineeringfromNorthChinaElectricPowerUniversity,BaoDing,China,theMaster’sdegreeinelectricalengineeringfromtheBeijingUniversityofTelecommunicationsandPosts,Beijing,China.HeiscurrentlyworkingtowardthePh.D.degreeinelec-tricalandcomputerengineeringattheUniversityofIllinois,Chicago.Hisresearchinterestsincludecommunication,sta-tisticalsignalprocessing,controlandautomation,andarticialintelligence. HoushangDarabireceivedtheB.S.degreeinindus-trialengineeringfromTechnologyandscienceUni-versity,Tehran,Iran,M.S.degreeinindustrialengin-erringfromSharifUniversityofTechnology,Tehran,Iran,andthePh.D.degreeinindustrialengineeringfromRutgersUniversity,NewBrunswick,NJ.HeiscurrentlyanAssociateProfessorwiththeDepartmentofMechanicalandIndustrialEngineer-ing,UniversityofIllinois,Chicago.Hisresearchin-terestsincludeapplicationofdiscrete-eventsystemscontroltheoryinmodelingandanalysisofserviceandmanufacturingsystems,computer-integratedmanufacturing,supplychainnetworks,andmanufacturinginformationsystems.Heistheauthorofmanypaperspublishedinseveraljournalsandconferenceproceedings.Dr.DarabiisaSeniorMemberoftheInstituteofIndustrialEngineers,andamemberoftheInstituteforOperationsResearchandtheManagementSciences. PatBanerjeereceivedtheB.Tech.degreeinmechan-icalengineeringfromtheIndianInstituteofTechnol-ogy,Kanpur,in1984,andtheM.S.andPh.D.degreesinindustrialengineeringfromPurdueUniversity,WestLafayette,IN,in1987and1990,respectively.HeiscurrentlyaProfessorintheDepartmentofMechanicalandIndustrialEngineeringandofCom-puterSciencewiththeUniversityofIllinois,Chicago.Hewasonthe2002ResearchVisionaryBoardatMotorolaLaboratories.Hiscurrentresearchinterestsincludevirtualrealityapplications;hapticsapplica-tions;sensors,diagnostics,andprognostics;immersivelearningeffectivenessanddisplayinterfaces;andlinearandnonlineardesignoptimizationmodels.Heistheauthorofover100publications,includingatextbookVirtualManufac-(Wiley,2001).HewasaDepartmentEditoroftheIIETransactionsProf.BanerjeeisaFellowoftheAmericanSocietyofMechanicalEngineers(ASME).HereceivedtheASMEMED/MHEDBestTechnicalPaperAward.HewasanAssociateEditoroftheIEEETRANSACTIONSONOBOTICSANDUTOMATION JingLiureceivedtheB.S.degreeinchemicalen-gineeringfromtheHebeiUniversityofTechnology,Tianjin,China,theM.S.degreeinmechanicalen-gineeringfromtheBeijingUniversityofPostsandTelecommunications,Beijing,China,andthePh.D.degreeinindustrialengineeringfromtheUniversityofIllinois,Chicago,in2005.SheiscurrentlyaResearcherinGeneralMotorsR&DCenter,Warren,MI,USA.Herresearchin-terestsincludeapplicationofdiscrete-eventsystemscontroltheoryinmodelingandanalysisofmanufac-turingsystems,andplantoorsystemcontrols.Dr.LiuisamemberoftheInstituteofElectricalandElectronicsEngineers,PhiKappaPhi,andtheInstituteofIndustrialEngineers.