外文翻译-基于wifi构建的智能家居系统综合接入及控制装置(嵌入式系统部分)

外文翻译原文APYROELECTRICINFRAREDSENSORBASEDINDOORLOCATIONAWARESYSTEMFORTHESMARTHOMESUKLEE,MEMBER,IEEE,KYOUNGNAMHA,KYUNGCHANGLEE,MEMBER,IEEEABSTRACTSMARTHOMEISEXPECTEDTOOFFERVARIOUSINTELLIGENTSERVICESBYRECOGNIZINGRESIDENTSALONGWITHTHEIRLIFESTYLEANDFEELINGSONEOFTHEKEYISSUESFORREALIZINGTHESMARTHOMEISHOWTODETECTTHELOCATIONSOFRESIDENTSCURRENTLY,THERESEARCHEFFORTISFOCUSEDONTWOAPPROACHESTERMINALBASEDANDNONTERMINALBASEDMETHODSTHETERMINALBASEDMETHODEMPLOYSATYPEOFDEVICETHATSHOULDBECARRIEDBYTHERESIDENTWHILETHENONTERMINALBASEDMETHODREQUIRESNOSUCHDEVICETHISPAPERPRESENTSANOVELNONTERMINALBASEDAPPROACHUSINGANARRAYOFPYROELECTRICINFRAREDSENSORSPIRSENSORSTHATCANDETECTRESIDENTSTHEFEASIBILITYOFTHESYSTEMISEVALUATEDEXPERIMENTALLYONATESTBEDINDEXTERMSSMARTHOME,LOCATIONBASEDSERVICE,PYROELECTRICINFRAREDSENSORPIRSENSOR,LOCATIONRECOGNITIONALGORITHMIINTRODUCTIONTHEREISAGROWINGINTERESTINSMARTHOMEASAWAYTOOFFERACONVENIENT,COMFORTABLE,ANDSAFERESIDENTIALENVIRONMENT1,2INGENERAL,THESMARTHOMEAIMSTOOFFERAPPROPRIATEINTELLIGENTSERVICESTOACTIVELYASSISTINTHERESIDENTSLIFESUCHASHOUSEWORK,AMUSEMENT,REST,ANDSLEEPHENCE,INORDERTOENHANCETHERESIDENTSCONVENIENCEANDSAFETY,DEVICESSUCHASHOMEAPPLIANCES,MULTIMEDIAAPPLIANCES,ANDINTERNETAPPLIANCESSHOULDBECONNECTEDVIAAHOMENETWORKSYSTEM,ASSHOWNINFIG1,ANDTHEYSHOULDBECONTROLLEDORMONITOREDREMOTELYUSINGATELEVISIONTVORPERSONALDIGITALASSISTANTPDA3,4FIG1ARCHITECTUREOFTHEHOMENETWORKSYSTEMFORSMARTHOMEESPECIALLY,ATTENTIONHASBEENFOCUSEDONLOCATIONBASEDSERVICESASAWAYTOOFFERHIGHQUALITYINTELLIGENTSERVICES,WHILECONSIDERINGHUMANFACTORSSUCHASPATTERNOFLIVING,HEALTH,ANDFEELINGSOFARESIDENT57THATIS,IFTHESMARTHOMECANRECOGNIZETHERESIDENTSPATTERNOFLIVINGORHEALTH,THENHOMEAPPLIANCESSHOULDBEABLETOANTICIPATETHERESIDENTSNEEDSANDOFFERAPPROPRIATEINTELLIGENTSERVICEMOREACTIVELYFOREXAMPLE,INAPASSIVESERVICEENVIRONMENT,THERESIDENTCONTROLSTHEOPERATIONOFTHEHVACHEATING,VENTILATING,ANDAIRCONDITIONINGSYSTEM,WHILETHESMARTHOMEWOULDCONTROLTHETEMPERATUREANDHUMIDITYOFAROOMACCORDINGTOTHERESIDENTSCONDITIONVARIOUSINDOORLOCATIONAWARESYSTEMSHAVEBEENDEVELOPEDTORECOGNIZETHERESIDENTSLOCATIONINTHESMARTHOMEORSMARTOFFICEINGENERAL,INDOORLOCATIONAWARESYSTEMSHAVEBEENCLASSIFIEDINTOTHREETYPESACCORDINGTOTHEMEASUREMENTTECHNOLOGYTRIANGULATION,SCENEANALYSIS,ANDPROXIMITYMETHODS8THETRIANGULATIONMETHODUSESMULTIPLEDISTANCESFROMMULTIPLEKNOWNPOINTSEXAMPLESINCLUDEACTIVEBADGES9,ACTIVEBATS10,ANDEASYLIVING11,WHICHUSEINFRAREDSENSORS,ULTRASONICSENSORS,ANDVISIONSENSORS,RESPECTIVELYTHESCENEANALYSISMETHODEXAMINESAVIEWFROMAPARTICULARVANTAGEPOINTREPRESENTATIVEEXAMPLESOFTHESCENEANALYSISMETHODAREMOTIONSTAR12,WHICHUSESADCMAGNETICTRACKER,ANDRADAR13,WHICHUSESIEEE80211WIRELESSLOCALAREANETWORKLANFINALLY,THEPROXIMITYMETHODMEASURESNEARNESSTOAKNOWNSETOFPOINTSANEXAMPLEOFTHEPROXIMITYMETHODISSMARTFLOOR14,WHICHUSESPRESSURESENSORSALTERNATIVELY,INDOORLOCATIONAWARESYSTEMSCANBECLASSIFIEDACCORDINGTOTHENEEDFORATERMINALTHATSHOULDBECARRIEDBYTHERESIDENTTERMINALBASEDMETHODS,SUCHASACTIVEBATS,DONOTRECOGNIZETHERESIDENTSLOCATIONDIRECTLY,BUTPERCEIVETHELOCATIONOFADEVICECARRIEDBYTHERESIDENT,SUCHASANINFRAREDTRANSCEIVERORRADIOFREQUENCYIDENTIFICATIONRFIDTAGTHEREFORE,ITISIMPOSSIBLETORECOGNIZETHERESIDENTSLOCATIONIFHEORSHEISNOTCARRYINGTHEDEVICEINCONTRAST,NONTERMINALMETHODSSUCHASEASYLIVINGANDSMARTFLOORCANFINDTHERESIDENTSLOCATIONWITHOUTSUCHDEVICESHOWEVER,EASYLIVINGCANBEREGARDEDTOINVADETHERESIDENTSPRIVACYWHILETHESMARTFLOORHASDIFFICULTYWITHEXTENDIBILITYANDMAINTENANCETHISPAPERPRESENTSANONTERMINALBASEDLOCATIONAWARESYSTEMTHATUSESANARRAYOFPYROELECTRICINFRAREDPIRSENSORS15,16THEPIRSENSORSONTHECEILINGDETECTTHEPRESENCEOFARESIDENTANDARELAIDOUTSOTHATDETECTIONAREASOFADJACENTSENSORSOVERLAPBYCOMBININGTHEOUTPUTSOFMULTIPLEPIRSENSORS,THESYSTEMISABLETOLOCATEARESIDENTWITHAREASONABLEDEGREEOFACCURACYTHISSYSTEMHASINHERENTADVANTAGEOFNONTERMINALBASEDMETHODSWHILEAVOIDINGPRIVACYANDEXTENDIBILITY,MAINTENANCEISSUESINORDERTODEMONSTRATEITSEFFICACY,ANEXPERIMENTALTESTBEDHASBEENCONSTRUCTED,ANDTHEPROPOSEDSYSTEMHASBEENEVALUATEDEXPERIMENTALLYUNDERVARIOUSEXPERIMENTALCONDITIONSTHISPAPERISORGANIZEDINTOFOURSECTIONS,INCLUDINGTHISINTRODUCTIONSECTIONIIPRESENTSTHEARCHITECTUREOFTHEPIRSENSORBASEDINDOORLOCATIONAWARESYSTEMPILAS,ANDTHELOCATIONRECOGNITIONALGORITHMSECTIONIIIDESCRIBESARESIDENTDETECTIONMETHODUSINGPIRSENSORS,ANDEVALUATESTHEPERFORMANCEOFTHESYSTEMUNDERVARIOUSCONDITIONSUSINGANEXPERIMENTALTESTBEDFINALLY,ASUMMARYANDTHECONCLUSIONSAREPRESENTEDINSECTIONIVIIARCHITECTUREOFTHEPIRSENSORBASEDINDOORLOCATIONAWARESYSTEMAFRAMEWORKOFTHESMARTHOMEGIVENTHEINDOORENVIRONMENTOFTHESMARTHOME,ANINDOORLOCATIONAWARESYSTEMMUSTSATISFYTHEFOLLOWINGREQUIREMENTSFIRST,THELOCATIONAWARESYSTEMSHOULDBEIMPLEMENTEDATARELATIVELYLOWCOSTBECAUSEMANYSENSORSHAVETOBEINSTALLEDINROOMSOFDIFFERENTSIZESTODETECTTHERESIDENTINTHESMARTHOMESECOND,SENSORINSTALLATIONMUSTBEFLEXIBLEBECAUSETHESHAPEOFEACHROOMISDIFFERENTANDTHEREAREOBSTACLESSUCHASHOMEAPPLIANCESANDFURNITURE,WHICHPREVENTTHENORMALOPERATIONOFSENSORSTHETHIRDREQUIREMENTISTHATTHESENSORSFORTHELOCATIONAWARESYSTEMHAVETOBEROBUSTTONOISE,ANDSHOULDNOTBEAFFECTEDBYTHEIRSURROUNDINGSTHISISBECAUSETHESMARTHOMECANMAKEUSEOFVARIOUSWIRELESSCOMMUNICATIONMETHODSSUCHASWIRELESSLANORRADIOFREQUENCYRFSYSTEMS,WHICHPRODUCEELECTROMAGNETICNOISE,ORTHEREMAYBESIGNIFICANTCHANGESINLIGHTORTEMPERATURETHATCANAFFECTSENSORPERFORMANCEFINALLY,ITISDESIRABLETHATTHESYSTEMSACCURACYISADJUSTABLEACCORDINGTOROOMTYPESAMONGMANYSYSTEMSTHATSATISFYTHEREQUIREMENT,THEPIRSENSORBASEDSYSTEMHASNOTATTRACTEDMUCHATTENTIONEVENTHOUGHTHESYSTEMHASSEVERALADVANTAGESTHEPIRSENSORS,WHICHHAVEBEENUSEDTOTURNONALIGHTWHENITDETECTSHUMANMOVEMENT,ARELESSEXPENSIVETHANMANYOTHERSENSORSINADDITION,BECAUSEPIRSENSORSDETECTTHEINFRAREDWAVELENGTHEMITTEDFROMHUMANSBETWEEN94104M,THEYAREREASONABLYROBUSTTOTHEIRSURROUNDINGS,INTERMSOFTEMPERATURE,HUMIDITY,ANDELECTROMAGNETICNOISEMOREOVER,ITISPOSSIBLETOCONTROLTHELOCATIONACCURACYOFTHESYSTEMBYADJUSTINGTHESENSINGRADIUSOFAPIRSENSOR,ANDPIRSENSORSAREEASILYINSTALLEDONTHECEILING,WHERETHEYARENOTAFFECTEDBYTHESTRUCTUREOFAROOMORANYOBSTACLESFIGURE2SHOWSTHEFRAMEWORKFORTHEPILASINASMARTHOMETHATOFFERSLOCATIONBASEDINTELLIGENTSERVICESTOARESIDENTWITHINTHISFRAMEWORK,VARIOUSDEVICESARECONNECTEDVIAAHOMENETWORKSYSTEM,INCLUDINGPIRSENSORS,ROOMTERMINALS,ASMARTHOMESERVER,ANDHOMEAPPLIANCESHERE,EACHROOMISREGARDEDASACELL,ANDTHEAPPROPRIATENUMBEROFPIRSENSORSISINSTALLEDONTHECEILINGOFEACHCELLTOPROVIDESUFFICIENTLOCATIONACCURACYFORTHELOCATIONBASEDSERVICESEACHPIRSENSORATTEMPTSTODETECTTHERESIDENTATACONSTANTPERIOD,ANDTRANSMITSITSSENSINGINFORMATIONTOAROOMTERMINALVIATHEHOMENETWORKSYSTEMFIG2FRAMEWORKOFSMARTHOMEFORTHEPILASCONSEQUENTLY,THEROOMTERMINALRECOGNIZESTHERESIDENTSLOCATIONBYINTEGRATINGTHESENSORINFORMATIONRECEIVEDFROMALLOFTHESENSORSBELONGINGTOONECELL,ANDTRANSMITSTHERESIDENTSLOCATIONTOTHESMARTHOMESERVERTHATCONTROLSTHEHOMEAPPLIANCESTOOFFERLOCATIONBASEDINTELLIGENTSERVICESTOTHERESIDENTWITHINTHISFRAMEWORK,THESMARTHOMESERVERHASTHEFOLLOWINGFUNCTIONS1THEVIRTUALMAPGENERATORMAKESAVIRTUALMAPOFTHESMARTHOMEGENERATINGAVIRTUALMAP,ANDWRITESTHELOCATIONINFORMATIONOFTHERESIDENT,WHICHISRECEIVEDFROMAROOMTERMINAL,ONTHEVIRTUALMAPWRITINGTHERESIDENTSLOCATIONTHEN,ITMAKESAMOVINGTRAJECTORYOFTHERESIDENTBYCONNECTINGTHESUCCESSIVELOCATIONSOFTHERESIDENTTRACKINGTHERESIDENTSMOVEMENT2THEHOMEAPPLIANCECONTROLLERTRANSMITSCONTROLCOMMANDSTOHOMEAPPLIANCESVIATHEHOMENETWORKSYSTEMTOPROVIDEINTELLIGENTSERVICESTOTHERESIDENT3THEMOVINGPATTERNPREDICTORSAVESTHECURRENTMOVEMENTTRAJECTORYOFTHERESIDENT,THECURRENTACTIONOFHOMEAPPLIANCES,ANDPARAMETERSREFLECTINGTHECURRENTHOMEENVIRONMENTSUCHASTHETIME,TEMPERATURE,HUMIDITY,ANDILLUMINATIONAFTERSTORINGSUFFICIENTINFORMATION,ITMAYBEPOSSIBLETOOFFERHUMANORIENTEDINTELLIGENTSERVICESINWHICHTHEHOMEAPPLIANCESSPONTANEOUSLYPROVIDESERVICESTOSATISFYHUMANNEEDSFOREXAMPLE,IFTHESMARTHOMESERVER“KNOWS”THATTHERESIDENTNORMALLYWAKESUPAT700AMANDTAKESASHOWER,ITMAYBEPOSSIBLETOTURNONTHELAMPSANDSOMEMUSICINADDITION,THETEMPERATUREOFTHESHOWERWATERCANBESETAUTOMATICALLYFORTHERESIDENTBLOCATIONRECOGNITIONALGORITHMINORDERTODETERMINETHELOCATIONOFARESIDENTWITHINAROOM,ANARRAYOFPIRSENSORSAREUSEDASSHOWNINFIG3INTHEFIGURE,THESENSINGAREAOFEACHPIRSENSORISSHOWNASACIRCLE,ANDTHESENSINGAREASOFTWOORMORESENSORSOVERLAPCONSEQUENTLY,WHENARESIDENTENTERSONEOFTHESENSINGAREAS,THESYSTEMDECIDESWHETHERHE/SHEBELONGSTOANYSENSINGAREABYINTEGRATINGTHESENSINGINFORMATIONCOLLECTEDFROMALLOFTHEPIRSENSORSINTHEROOMFOREXAMPLE,WHENARESIDENTENTERSTHESENSINGAREAB,SENSORSAANDBOUTPUTONSIGNALS,WHILESENSORCOUTPUTSOFFSIGNALAFTERCOLLECTINGOUTPUTS,THEALGORITHMCANINFERTHATTHERESIDENTBELONGSTOTHESENSINGAREABACCORDINGTOTHENUMBEROFSENSORSANDTHEARRANGEMENTOFTHESENSORSSIGNALINGON,THERESIDENTSLOCATIONISDETERMINEDINTHEFOLLOWINGMANNERFIRST,IFONLYONESENSOROUTPUTSONSIGNAL,THERESIDENTISREGARDEDTOBEATTHECENTEROFTHESENSINGAREAOFTHECORRESPONDINGSENSORIFTHEOUTPUTSOFTWOADJACENTSENSORSAREON,THERESIDENTSLOCATIONISASSUMEDTOBEATTHEPOINTMIDWAYBETWEENTHETWOSENSORSFINALLY,IFTHREEORMORESENSORSSIGNALON,THERESIDENTISLOCATEDATTHECENTROIDOFTHECENTERSOFTHECORRESPONDINGSENSORSFOREXAMPLE,ITISASSUMEDTHATTHERESIDENTISLOCATEDATPOINT1INTHEFIGUREWHENONLYSENSORASIGNALSON,WHILETHERESIDENTISLOCATEDATPOINT2WHENSENSORSAANDBBOTHOUTPUTONSIGNALSTHELOCATIONACCURACYOFTHISSYSTEMCANBEDEFINEDTHEMAXIMUMDISTANCEBETWEENTHEESTIMATEDPOINTSANDTHERESIDENTFOREXAMPLE,WHENARESIDENTENTERSSENSINGAREAA,THERESIDENTISASSUMEDTOBEATPOINT1ONTHEASSUMPTIONTHATARESIDENTCANBEREPRESENTEDBYAPOINTANDTHERADIUSOFTHESENSINGAREAOFAPIRSENSORIS1M,WEKNOWTHATTHELOCATIONACCURACYIS1MBECAUSETHEMAXIMUMERROROCCURSWHENTHERESIDENTISONTHEBOUNDARYOFSENSINGAREAAALTERNATIVELY,WHENTHERESIDENTISINSENSINGAREAB,THERESIDENTISASSUMEDTOBEATPOINT2,ANDTHEMAXIMUMLOCATIONERROROCCURSWHENTHERESIDENTISACTUALLYATPOINT3INTHISCASE,THEERRORIS3/2MWHICHISTHEDISTANCEBETWEENPOINTS2AND3THEREFORE,THELOCATIONACCURACYOFTHETOTALSYSTEMSHOWNINFIG3CANBEREGARDEDAS1M,WHICHISTHEMAXIMUMVALUEOFTHELOCATIONACCURACYOFEACHAREASINCETHENUMBEROFSENSORSANDTHESIZEOFTHEIRSENSINGAREASDETERMINETHELOCATIONACCURACYOFTHEPILAS,ITISNECESSARYTOARRANGETHEPIRSENSORSPROPERLYTOGUARANTEETHESPECIFIEDSYSTEMACCURACYFIG3THELOCATIONRECOGNITIONALGORITHMFORPIRSENSORSINORDERTODETERMINETHERESIDENTSLOCATIONPRECISELYANDINCREASETHEACCURACYOFTHESYSTEM,ITISDESIRABLETOHAVEMORESENSINGAREASWITHGIVENNUMBEROFSENSORSANDTOHAVESENSINGAREASOFSIMILARSIZEFIG4SHOWSSOMEEXAMPLESOFSENSORARRANGEMENTSANDSENSINGAREASFIG4AAND4BSHOWTHEARRANGEMENTSWITHNINESENSORSTHATPRODUCE40AND21SENSINGAREAS,RESPECTIVELYTHEARRANGEMENTINFIG4AISBETTERTHANFIG4BINTERMSIFTHENUMBEROFSENSINGAREASHOWEVER,THEARRANGEMENTINFIG4AHASSOMEAREASWHEREARESIDENTCANNOTBEDETECTEDANDLOWERLOCATIONACCURACYTHANTHATINFIG4BFIG4CSHOWSANARRANGEMENTWITHTWELVESENSORSTHATFIVE28SENSINGAREASWITHOUTANYBLINDSPOTSFIG4LOCATIONACCURACYACCORDINGTOTHESENSORARRANGEMENTOFPIRSENSORSA40SENSINGAREASB21SENSINGAREASC28SENSINGAREASWITHTWELVESENSORSWHENPIRSENSORSAREINSTALLEDAROUNDTHEEDGEOFAROOM,ASSHOWNINFIG4C,ITSOMETIMESMAYGIVEAWKWARDRESULTSONEEXAMPLEISSHOWNINFIG5FIG5ASHOWSTHEPATHOFARESIDENTIFWEMARKTHEESTIMATEDPOINTSBYUSINGTHESENSORLOCATIONORTHEMIDPOINTOFADJACENTSENSORS,ITWILLBEAZIGZAGGINGPATTERNSASSHOWNINFIG5BINORDERTOALLEVIATETHIS,WEMAYREGARDTHESENSORSONTHEEDGESTOBELOCATEDALITTLEINWARDS,WHICHGIVETHERESULTSHOWNINFIG5CFIG5THEEFFECTOFCOMPENSATINGFORTHECENTERPOINTOFTHEOUTERSENSORSARESIDENTSMOVEMENTBBEFORECOMPENSATINGFORTHEOUTERSENSORSCAFTERCOMPENSATINGFORTHEOUTERSENSORSSUMMARYANDCONCLUSIONSTHISPAPERPRESENTSAPIRSENSORBASEDINDOORLOCATIONAWARESYSTEMTHATESTIMATESTHERESIDENTSLOCATIONFORLOCATIONBASEDINTELLIGENTSERVICESINTHESMARTHOMETHISPAPERINTRODUCESTHEFRAMEWORKOFSMARTHOMEFORTHELOCATIONAWARESYSTEM,ANDALOCATIONRECOGNITIONALGORITHMTHATINTEGRATESTHEINFORMATIONCOLLECTEDFROMPIRSENSORSINADDITION,THISPAPERPRESENTSARESIDENTDETECTIONMETHODFINALLY,ANEXPERIMENTISIMPLEMENTEDTOEVALUATETHEEFFICACYOFTHEPILASBASEDONSEVERALEXPERIMENTSCONDUCTEDUNDERVARIOUSCONDITIONS,WEVERIFIEDTHATTHEPILASCANESTIMATESRESIDENTSLOCATIONSUFFICIENTLYWELLMOREOVER,BECAUSETHELOCATIONACCURACYOFTHESYSTEMISLESSTHAN05MWITHOUTANYTERMINALFORLOCATIONRECOGNITION,THESYSTEMCANBEVERYPRACTICALFURTHERMORE,ITSHOULDBEPOSSIBLETOENHANCETHELOCATIONACCURACYOFTHESYSTEMBYINCREASINGTHENUMBEROFSENSINGAREAS,BYEQUALIZINGTHESENSINGAREASBASEDONTHESENSORARRANGEMENT,ORBYCOMPENSATINGFORTHECENTERSOFOUTERSENSORSSINCETHELOCATIONACCURACYOFTHISSYSTEMDIFFERSACCORDINGTOTHESENSORARRANGEMENT,ITISNECESSARYTODETERMINETHEOPTIMALSENSORARRANGEMENTTHATOFFERSTHEGREATESTLOCATIONACCURACYINORDERTOENHANCETHELOCATIONACCURACY,ITISALSONECESSARYTOENHANCETHEMETHODOFPROCESSINGTHEPIRSENSORSUSINGMOREADVANCEDTECHNIQUESSUCHASPROBABILISTICTHEORIESANDSOFTCOMPUTINGFINALLY,THEPROPOSEDPILASYSTEMSHOULDBEEXTENDEDTODEALWITHAROOMOCCUPIEDBYMORETHANONERESIDENTS外文资料翻译基于热释电红外传感器的智能家居室内感应定位系统SUKLEE，电机及电子学工程师联合会会员KYOUNGNAMHA,KYUNGCHANGLEE,电机及电子学工程师联合会会员摘要智能家居，是一种可以通过识别具有不同生活习惯和感觉的住户来提供各种不同的智能服务。而实现这样的功能其中最关键的问题之一就是如何确定住户的位置。目前，研究工作只要集中于两种方法终端方式和非终端方式。终端方式需要一种住户随身携带的设备，而非终端方式则不需要这样的设备。本文提出一种使用可以探测到住户的热释电红外传感器（红外传感器）的新的非终端方式。该系统的可行性已经通过了测试平台的实验性评估。索引词智能家居，定位服务，热释电红外传感器（红外传感器），定位识别算法I简介现在由于人人都想有一个方便，舒适，安全的居住环境，因此大家对于智能家居表现的越来越感兴趣12。一般来说，智能家居旨在提供合适的智能服务来积极促进住户更好的生活，比如家务劳动，娱乐，休息和睡眠。因此，为了提高住户的便捷和安全，像家用电器，多媒体设备和互联网设备应通过家庭网络系统连接在一起，如图1所示。并且它们应通过电视或个人数字助理（PDA）来控制或远程监控34。图1智能家居的家庭网络体系结构尤其要注意的是，作为一种提供高质量的智能服务，目标应集中于定位服务，同时考虑人为因素，比如住户的生活方式，健康状况和居住感受57。也就是说，如果智能家居能识别住户的生活方式或健康状况，那么家用电器应该能预见住户的需要，并能更主动的提供适合的智能服务。例如，在一个被动的服务环境下，需要住户控制供热通风与空气调节系统（供暖，通风和空调），而智能家居将根据住户情况自动调节房间的温湿度。智能家居或智能办公室的各种室内感应定位系统的已经研发到能够识别住户的位置。一般来说，室内定位感应系统根据测量技术分为三种类型三角测量，场景分析和接近方法8。三角测量法是通过多个已知点来计算位置距离。运用三角测量法的例子包括ACTIVEBADGES9，ACTIVEBATS10和EASYLIVING11，它们分别运用了红外传感器，超声波传感器和视觉传感器来实现的。场景解析法是检测一个场景内的特定着眼点。场景解析法的典型例子是使用直流磁力跟踪器的MOTIVESTAR12，和使用无线局域网络LAN标准IEEE802,11的RADAR13。接近法则是以一组已知点中最接近的点近似作为定位点。接近法的例子有使用压力传感器的SMARTFLOOR14。另外，室内感应定位系统可以根据是否需要住户随身携带一种设备来分类。终端方式，例如ACTIVEBATS，不需要直接找到住户位置，但是可以感应到住户随身携带的设备位置，例如红外收发器或者射频识别技术（RFID）标签。因此，如果住户没有随声携带终端设备，那就不可能找到他。相反的，非终端方式如EASYLIVING和SMARTFLOOR则不需要这种设备就能找到住户位置。然而，人们认为EASYLIVING侵犯了住户隐私，SMARTFLOOR则是扩展和维护都比较困难。本文提出一种使用阵列热释电红外（PIR）传感器实现的基于非终端方式的室内感应定位系统1516。红外传感器固定在天花板上，并使相邻的传感器的感应范围有重叠。当它感应到一名住户时，通过多个红外传感器的综合，能够比较准确的确定住户的位置。该系统不仅具有非终端方式的特有优点，还避免了侵犯隐私，扩展性不佳和维护困难的问题。为了证明其有效性，已经在实验平台上通过了各种不同测试环境下的实验性评估。包括此简介，本文共分为四个部分，第二部分介绍基于红外传感器的室内定位感应系统架构（PILAS）以及定位识别算法。第三部分介绍了基于红外传感器的住户检测法和在实验测试平台上的不同环境下评估系统的表现。最后一部分为总结和结论。II基于热释电红外传感器的室内感应定位系统架构A智能家居的结构鉴于智能家居的室内环境，室内感应定位系统必须满足一下条件。第一，由于需要在各种大小不同的房间里安装大量传感器来感知智能家居中的住户，因此定位感应系统需保持较低的成本。第二，传感器的安装必须是灵活可变的，因为各个房间的形状结构不同，并且还有各样阻碍传感器正常工作的家电和家具。第三，要求定位感应系统使用的传感器能够抵御很强的噪声，这是因为智能家居能利用各种无线传输技术，比如无线局域网，射频系统，它们都会产生电磁噪声，并且光或温度的巨大变化也会影响传感器的正常工作。最后该系统的精度可以，根据房间类型作出最合适的调节。尽管基于热释电红外传感器的这个系统有诸多的优点，但在众多满足要求的产品中并不能吸引人们更多的关注。它已应用于感应灯（当它感应到人体移动时使灯自动打开），并且成本低于许多其他种类的感应器。另外，由于热释电红外传感器感应的是人体发出的94104微米波长的红外线，从温度、湿度和电磁噪声来说，这种波长相对周围环境较为明显。而且，它可以通过调整感应半径来控制定位精度，并容易安装在天花板上，这样就不会受到房间结构和障碍物的影响。图2显示的是为住户提供基于位置的智能服务的PILAS智能家居框架。在这个框架下，包括热释电红外传感器、房屋终端、智能家居服务器和家用电器在内的各种设备通过家庭网络系统连接在一起。每个房间被视为一个单元，并在每个单元的天花板上安装适当数量的传感器，为定位服务提供足够的定位精度。每个红外传感器周期性的感应住户位置，然后将感应信息通过家庭网络系统传输到房屋终端。因此，房屋终端通过集合来自同一个单元的传感器信息来确定住户的位置，再将住户位置传输到智能家居服务器，服务器就会控制家用电器为住户提供基于位置的定位服务。图2PILAS智能家居框架在这个框架内，智能家居服务器具有以下功能（1）虚拟地图发生器为智能家居提供虚拟地图（生成虚拟地图），并在虚拟地图中标出由房屋终端提供的住户位置信息（标注住户位置）。然后，它通过连接住户的连续定位点来绘制住户的运动轨迹（追踪住户运动）。（2）家电控制器通过家庭网络系统发送控制命令给家用电器为住户提供智能服务。（3）运动模式预测器保存当前的住户运动轨迹、家电的动作和反映居家环境的参数，比如时间、温度、湿度、光照度。储存足够的信息后，它可能会使家电主动提供满足人们需要的人性化的