基于超限学习机的无设备定位方法研究

基于超限学习机的无设备定位方法研究基于超限学习机的无设备定位方法研究

摘要

无线定位技术因其方便快捷、无需硬件部署、精度高等优点而受到广泛关注。本文提出一种基于超限学习机的无设备定位方法，其中超限学习机被用于实现非线性函数映射，通过收集Wi-Fi信号强度和位置数据作为训练集，并以压缩感知的方式实现极限降维，来进行定位。为了进一步提升精度，本文引入了局部权重贡献方法来降低信号强度测量误差对定位结果的影响。

本文还在室内环境下进行了一系列实验，比较了所提出的方法与传统的KNN定位算法和基于支持向量机的定位方法。实验结果表明，所提出的无设备定位方法具有较高的定位精度和更好的鲁棒性。

关键词：超限学习机；无设备定位；Wi-Fi；压缩感知；局部权重贡献。

Abstract

Wirelesspositioningtechnologyhasattractedwidespreadattentionduetoitsconvenience,nohardwaredeployment,andhighaccuracy.Inthispaper,adevice-freepositioningmethodbasedonextremelearningmachine(ELM)isproposed,inwhichtheELMisusedtoachievenon-linearfunctionmapping.Wi-Fisignalstrengthandlocationdataarecollectedastrainingsets,andextremedimensionreductionisachievedbycompressivesensingtoperformpositioning.Inordertofurtherimprovetheaccuracy,thispaperintroducesthelocalweightcontributionmethodtoreducetheimpactofmeasurementerrorsonthepositioningresults.

Inaddition,aseriesofexperimentswerecarriedoutinanindoorenvironmenttocomparetheproposedmethodwiththetraditionalKNNpositioningalgorithmandthesupportvectormachine-basedpositioningmethod.Theexperimentalresultsshowthattheproposeddevice-freepositioningmethodhashigherpositioningaccuracyandbetterrobustness.

Keywords:Extremelearningmachine(ELM);device-freepositioning;Wi-Fi;compressivesensing;localweightcontributionDevice-freepositioninghasbecomeanimportantresearchareaduetoitswiderangeofapplicationssuchassecurity,healthcare,andhomeautomation.Inthisstudy,anoveldevice-freepositioningalgorithmbasedonextremelearningmachine(ELM)andcompressivesensingwasproposed.Theproposedalgorithmutilizesthereceivedsignalstrength(RSS)ofWi-Fisignalstoestimatethepositionofatargetuserwithouttheneedforanyadditionaldevicesorsensors.

TheELMalgorithmwasutilizedtotrainalocalweightcontributionmatrix,whichisusedtodeterminethecontributionofeachsignalstrengthmeasurementtothepositioningresults.CompressivesensingwasusedtoreducethedimensionalityoftheRSSmatrix,thusreducingthecomputationalcomplexityandimprovingtheaccuracyofthealgorithm.

Aseriesofexperimentswereconductedinanindoorenvironmenttoevaluatetheproposeddevice-freepositioningmethod.TheexperimentalresultsshowedthattheproposedmethodoutperformedthetraditionalKNNpositioningalgorithmandthesupportvectormachine-basedpositioningmethodintermsofaccuracyandrobustness.

Inconclusion,thisstudyproposesanoveldevice-freepositioningalgorithmbasedonELMandcompressivesensing,whichcanaccuratelyestimatethepositionofatargetuserusingonlyWi-Fisignals.Themethodhaspotentialforawiderangeofapplications,includinghomeautomation,healthcare,andsecurityTherearesomelimitationsandfuturedirectionsfortheproposeddevice-freepositioningalgorithmbasedonELMandcompressivesensing.First,thealgorithmassumesthattheenvironmentisstaticduringthepositioningprocess.However,inreal-worldscenarios,theenvironmentmaychangedynamicallyovertime,whichcouldaffecttheaccuracyofthealgorithm.Therefore,futureresearchcanfocusondevelopingdynamicalgorithmsthatcanadapttochangingenvironments.

Second,thealgorithmisbasedonWi-Fisignals,whichmaynotbeavailableinallenvironments.Insuchcases,alternativesignals,suchasBluetoothorRFID,couldbeused.Futureresearchcanexplorehowtheproposedalgorithmcouldbeadaptedtoworkwithothertypesofsignals.

Third,theproposedalgorithmrequiresatrainingphasetobuildthedictionarymatrix.Thisprocesscanbetime-consumingandmaynotbefeasibleinsomereal-worldscenarios.Therefore,futureresearchcanfocusondevelopingalgorithmsthatdonotrequireatrainingphase.

Fourth,theproposedalgorithmcurrentlyonlyworksforsingle-userscenarios.Inmulti-userenvironments,interferencebetweenuserscouldaffecttheaccuracyofthealgorithm.Therefore,futureresearchcanexplorehowthealgorithmcouldbeadaptedtoworkinmulti-userscenarios.

Finally,whiletheproposedalgorithmoutperformedtraditionalpositioningalgorithmsintermsofaccuracyandrobustness,thereisstillroomforimprovement.Futureresearchcanfocusondevelopingmoreadvancedalgorithmsthatfurtherimprovetheaccuracyandefficiencyofdevice-freepositioningsystems.

Overall,theproposeddevice-freepositioningalgorithmbasedonELMandcompressivesensinghasthepotentialtorevolutionizeindoorpositioningsystems.Withfurtherdevelopmentandresearch,itcouldenableawiderangeofapplicationsthatbenefitsocietyOnepotentialapplicationofdevice-freepositioningsystemsisinthefieldofhealthcare.Hospitalstaffneedtokeeptrackofpatientsandmedicalequipmentwithinthehospitalenvironment,andaccurateindoorpositioningcanhelptoincreaseefficiencyandreduceerrors.Forexample,adevice-freepositioningsystemcouldbeusedtotrackthemovementofahospitalbedandalertstaffwhenitreachesacertainlocation,suchastheoperatingroom.Itcouldalsobeusedtotrackthelocationofmedicalstaff,ensuringthattheyareinthecorrectareatoprovidetherequiredmedicalcare.

Anotherpotentialapplicationisinthefieldofsecurity.Traditionalsecuritysystemssuchasvideocamerasmaybeineffectiveincertainsituations,suchaswhentheintruderiswearingamaskorifthecamera'sviewisblocked.Adevice-freepositioningsystemcandetectthepresenceofahumanbeingeveniftheyarenotcarryinganyelectronicdevices,enablingsecuritypersonneltoidentifytheintruderandtakeappropriateaction.

Moreover,device-freepositioningsystemscanalsobeusedinenvironmentalmonitoring.Theycandetectandtrackthemovementofwildlifeinnaturalhabitatswithoutdisturbingthem,providingvaluableinformationtoresearchersandconservationists.Theycanalsobeusedtomonitorthemovementofpeopleindisasterzones,enablingfirstresponderstolocatesurvivorsandprovideassistancemoreefficiently.

Finally,device-freepositioningsystemscanbeusedinretailenvironments.Theycanprovidevaluableinsightsintocustomerbehavior,suchashowtheynavigatethestoreandwhichitemsaremostpopular.Thisinformationcanbeusedtoimprovestorelayoutandproductplacement,leadingtoincreasedsalesandcustomersatisfaction.

Inconclusion,device-freepositioningsystemshaveenormouspotentialtoenhance