IEEE Communications Magazine • February 2002112

Indoor GeolocationScience and Technology

0163-6804/02/$17.00 © 2002 IEEE

ABSTRACT

This article presents an overview of the techni-cal aspects of the existing technologies for wire-less indoor location systems. The two majorchallenges for accurate location finding in indoorareas are the complexity of radio propagation andthe ad hoc nature of the deployed infrastructurein these areas. Because of these difficulties a vari-ety of signaling techniques, overall system archi-tectures, and location finding algorithms areemerging for this application. This article pro-vides a fundamental understanding of the issuesrelated to indoor geolocation science that areneeded for design and performance evaluation ofemerging indoor geolocation systems.

INTRODUCTIONRecently, there is increasing interest in accuratelocation finding techniques and location-basedapplications for indoor areas. The Global Position-ing System (GPS) [1] and wireless enhanced 911(E-911) services [2] also address the issue of loca-tion finding. However, these technologies cannotprovide accurate indoor geolocation, which has itsown independent market and unique technicalchallenges. In 1997, while engaged in the DefenseAdvanced Research Projects Agency’s (DARPA’s)Small Unit Operation/Situation Awareness System(SUO/SAS) program, the lead author of this arti-cle and his research group noticed the need forfundamental research in accurate indoor geoloca-tion [3]. The follow-up initiative of the groupattracted the attention of Nokia and other Finnishorganizations to the commercial importance ofindoor geolocation. In recognition of this impor-tance, an NSF grant was awarded to establish a sci-entific foundation in this field.

Accurate indoor geolocation is an importantand novel emerging technology for commercial,public safety, and military applications [4]. In com-mercial applications for residential and nursinghomes there is an increasing need for indoorgeolocation systems to track people with specialneeds, the elderly, and children who are away from

visual supervision, to navigate the blind, to locatein-demand portable equipment in hospitals, and tofind specific items in warehouses. In public safetyand military applications, indoor geolocation sys-tems are needed to track inmates in prisons andnavigating policeman, fire fighters, and soldiers tocomplete their missions inside buildings. Theseincentives have initiated interest in modeling theradio channel for indoor geolocation applications[3, 5], development of new technologies [6], andemergence of first-generation indoor geolocationproducts [7]. To help the growth of this emergingindustry, there is a need to develop a scientificframework to lay a foundation for design and per-formance evaluation of such systems.

Figure 1 illustrates the functional block dia-gram of a wireless geolocation system. The mainelements of the system are a number of locationsensing devices that measure metrics related tothe relative position of a mobile terminal (MT)with respect to a known reference point (RP), apositioning algorithm that processes metricsreported by location sensing elements to esti-mate the location coordinates of MT, and a dis-play system that illustrates the location of theMT to users. The location metrics may indicatethe approximate arrival direction of the signal orthe approximate distance between the MT andRP. The angle of arrival (AOA) is the commonmetric used in direction-based systems. Thereceived signal strength (RSS), carrier signalphase of arrival (POA), and time of arrival(TOA) of the received signal are the metricsused for estimation of distance. As the measure-ments of metrics become less reliable, the com-plexity of the position algorithm increases. Thedisplay system can simply show the coordinatesof the MT, or it may identify the relative loca-tion of the MT in the layout of an area. This dis-play system could be software residing in aprivate PC or a mobile locating unit, locallyaccessible software in a local area network(LAN), or a universally accessible service on theWeb. Obviously, as the horizon of accessibility ofthe information increases, design of the displaysystem becomes more complex.

Kaveh Pahlavan and Xinrong Li, Worcester Polytechnic Institute

Juha-Pekka Mäkelä, University of Oulu, Finland

This work is supported inpart by the National Sci-ence Foundation underGrant ECS-0084112.

NEXT-GENERATION BROADBAND WIRELESSNETWORKS AND NAVIGATION SERVICES

IEEE Communications Magazine • February 2002 113

There are two basic approaches to designing awireless geolocation system. The first approach isto develop a signaling system and a network infra-structure of location sensors focused primarily ongeolocation application. The second approach is touse an existing wireless network infrastructure tolocate an MT. The advantage of the first approachis that physical specification, and consequentlyquality of the location sensing results, is under con-trol of the designer. The MT can be designed as avery small wearable tag or sticker, and the densityof the sensor infrastructure can be adjusted to therequired accuracy of the location finding applica-tion. The advantage of the second approach is thatit avoids expensive and time-consuming deploy-ment of infrastructure. These systems, however,need to use more intelligent algorithms to compen-sate for the low accuracy of the measured metrics.Both approaches have their own markets, anddesign work on both technologies has been pur-sued in the past few years [2, 4, 7, 8].

To develop a scientific foundation, we need toexamine the performance of different signalingtechniques and geolocation approaches. This per-formance evaluation needs a suitable model forradio propagation that reflects the characteristicsof the channel affecting the accuracy of locationsensing and system positioning. In the next threesections we address technical issues related tochannel modeling, location sensing, and position-ing algorithms for indoor geolocation systems.

CHANNEL CHARACTERISTICS FORINDOOR GEOLOCATION

The indoor radio propagation channel is charac-terized as site-specific, severe multipath, and lowprobability for availability of a line of sight(LOS) signal propagation path between thetransmitter and receiver [9]. The two majorsources of errors in the measurement of locationmetrics in indoor environment are multipathfading and no LOS (NLOS) conditions due toshadow fading [3].

Radio propagation channel models are devel-oped to provide a means to analyze the perfor-mance of a wireless receiver. The performancecriteria for telecommunication and geolocationsystems are quite different [3]. The performancecriterion for telecommunication systems is the biterror rate (BER) of the received data stream,while for geolocation systems the performancemeasure is the estimated accuracy of locationcoordinates. The accuracy of location estimationis a function of the accuracy of location metricsand the complexity of positioning algorithms.Since the metrics for geolocation applications areAOA, RSS, and TOA, models for geolocationapplication must reflect the effects of channelbehavior on the estimated value of these metricsat the receiver. The existing narrowband indoorradio channel models designed for telecommuni-cation applications [9] can be used to analyze theRSS for geolocation applications. The AOA partof the emerging 3D channel models developedfor smart antenna applications [10, 11] might beused for modeling of the AOA for indoor geolo-cation applications. However, the existing wide-band indoor multipath channel measurements

and models [9] are not suitable for analysis of thebehavior of TOA for geolocation applications.

The existing statistical wideband indoor multi-path models, such as the JTC model [9], repre-sent multipath characteristics of the channel witha discrete channel profile similar to the oneshown in Fig. 2. The strength and arrival time ofthe paths are so determined that the root meansquare (RMS) delay spread and consequentlyBER of a telecommunication receiver obtainedfrom the simulations using these profile repre-sents values similar to those obtained fromempirical measurements. If these models areused for performance evaluation of TOA-basedgeolocation systems, the statistics of distanceerrors do not reflect the results obtained fromempirical data [3]. Besides, to confirm the mod-eling results of a radio channel, empirical mea-surement is essential to check the validity of themodel. In the literature there are a number ofmeasurements of the wideband characteristics ofindoor radio channels for frequencies from 1 to60 GHz [9]. However, none of these measure-ments are useful for geolocation applicationsbecause they do not have a well-calibrated esti-mate of the arrival time of the direct LOS(DLOS) path and a very accurate measurementof the real physical distance between the trans-mitter and receiver [12]. The only available short-range measurements calibrated for geolocationapplications are those reported in [12], which areused in this article to analyze the performance ofsuper-resolution techniques in the next section.

While we do not have any good models forthe multipath characteristics of indoor radiochannels for geolocation applications, there arethree classes of recent statistical modelingapproaches that can be used to develop reliablemodels in the future, which are wideband 2D

� Figure 1. A functional block diagram of wireless geolocation systems.

Displaysystem

Locationsensing

.......

ReceivedRF signal

Location metrics:TOA, AOA,RSS, ...

Locationcoordinates(x, y, z)

Positioningalgorithm

Locationsensing

� Figure 2. The multipath profile of an indoor radio propagation channel.

Multipathprofile

RMS delay spread used for telecommunications

DLOS path strengthand relative strengthsof the other pathsused for geolocation

IEEE Communications Magazine • February 2002114

multipath modeling [3, 5], 3D geometrical statis-tical modeling [10], and 3D measurement-basedstatistical modeling [11]. In measurement-based2D statistical modeling, the measurement dataare used to define a multipath profile by

(1)

where Lp is the number of multipath components,and a k = |a k| e j f k and tk are complex ampli-tude and propagation delay of the kth path,respectively. The strength and statistical charac-teristics of the first path and its relative strengthwith respect to other paths fit similar resultsobtained from empirical data. The measurementsystems for this approach are the same as thoseused for telecommunication applications [7, 9].However, these systems are calibrated for accu-rate measurement of the TOA of the DLOS, andfor each measurement the physical distancebetween the transmitter and receiver is accuratelyrecorded. Preliminary measurement and model-ing work in this field is reported in [5, 12]; largercalibrated measurement databases and morepractical multipath models need further investiga-tion.

In 3D modeling, the mathematical model forthe channels is represented by

(2)

where qk is the AOA of the kth path [11]. Whilein 2D modeling each path was associated with aTOA, in 3D modeling each path is associated witha TOA and an AOA. The 3D models can bedeveloped either based on geometric analysis ofthe statistics of the paths arriving from differentdirections or out of empirical 3D channel mea-surement data. The 3D geometrical statisticalmodels, developed for smart antenna applications,use an analytical approach to relate propagationparameters to the structure of scattering in theenvironment [10]. In this approach, a mathemati-cal description of radio propagation based on sta-tistical building features and a geometric opticsapproximation of Maxwell’s equations areemployed to derive relevant radio propagationmodels such as distributions of the TOA, AOA,and RSS. The statistics of the AOA and RSS inthese models can be used directly for indoorgeolocation applications. Further research in thisarea is needed to develop statistical models for theTOA of the DLOS path and its relation to otherpaths to make them useful for the analysis of posi-tioning errors in TOA-based geolocation systems.

In 3D measurement-based statistical model-ing, measured channel characteristics are used todevelop models for AOA, TOA, and RSS. Themajor challenge of this approach is the imple-mentation of a system to measure the 3D charac-teristics of the channel. Recently two techniqueshave been studied for this purpose. The firsttechnique mechanically rotates a directionalantenna to measure the strength of the signalarriving from different directions, and the secondtechnique measures a set of eight channel

impulse responses using an antenna array andcalculates the AOA using signal processing tech-niques [11]. Preliminary 3D modeling of anindoor area using a limited database in a buildingis available in [11]. More extensive measurementand modeling in this field can result in realisticmodels for indoor geolocation applications.

LOCATION SENSING TECHNIQUESAs discussed in the introduction, the location sens-ing elements measure RSS, AOA, and TOA aslocation metrics. The indoor radio channel suffersfrom severe multipath propagation and heavyshadow fading, so the measurements of RSS andAOA provide less accurate metrics than does TOA[4]. As a result, similar to GPS systems, indepen-dent systems designed for indoor geolocation nor-mally employ the more accurate TOA as thelocation metric. Systems using existing infrastruc-tures installed for wireless LANs or the third-gen-eration (3G) indoor systems may use RSS, AOA,or less accurate TOA measurements to fully exploitthe existing hardware implementation designed fortraditional telecommunication applications [8]. Inindoor areas, due to obstruction by walls, ceilings,or other objects, the DLOS propagation path isnot always the strongest; in some cases (e.g.,NLOS), it may not even be detectable with a spe-cific receiver implementation [3]. In such cases,dramatically large errors occur in TOA estimation.To accurately estimate the TOA in indoor areas,we need to resort to different and more complexsignaling formats, frequency of operation, and sig-nal processing techniques that can resolve theproblems. The following subsection is devoted toaccurate TOA estimation techniques.

ESTIMATION OF TOA FOR INDOOR RANGINGThe TOA-based systems measure distance basedon an estimate of signal propagation delay (i.e.,TOA) between a transmitter and a receiver sincein free space or air, radio signals travel at theconstant speed of light. The TOA can be mea-sured by either measuring the phase of receivednarrowband carrier signal or directly measuringthe arrival time of a wideband narrow pulse. Thewideband pulses for measuring TOA can be gen-erated either directly [6] or using spread spec-trum technology [7]. In the following, we presentthese techniques in three classes: narrowband,wideband, and ultra wideband techniques.

Narrowband Signals and Phase Measure-ment Systems — In the narrowband rangingtechnique, the phase difference between receivedand transmitted carrier signals is used to measurethe distance between two points. The phase of areceived carrier signal, f, and the TOA of the sig-nal, t, are related by t = f/wc, where wc is the car-rier frequency in radian. It is well known that thedifferential GPS (DGPS) using measured refer-ence carrier phase at the receiver improves thelocation accuracy of the traditional GPS fromabout 20 m to within 1m [1]. However, unlike theDGPS, where the DLOS signal path is always pre-sent, the severe multipath condition of the indoorgeolocation environment causes substantial errorsin phase measurements. When a narrowband car-rier signal is transmitted in a multipath environ-

h k k kk

Lp

( , ) ( , ),t q a d t t q q= - -=

-

Â0

1

h k kk

Lp

( ) ( ),t a d t t= -=

-

Â0

1

In indoor areas,

due to

obstruction by

walls, ceilings, or

other objects,

the DLOS

propagation path

is not always the

strongest path

and even in some

casess, for

example, NLOS,

it may not be

detectable with a

specific receiver

implementation.

In such cases,

dramatically large

errors occur in

TOA estimation.

IEEE Communications Magazine • February 2002 115

ment, the composite received carrier signal is thesum of a number of carriers, arriving along differ-ent paths, of the same frequency but differentamplitude and phase. The frequency of the com-posite received signal remains unchanged, but thephase will be different from that of the DLOS sig-nal [9]. An immediate conclusion is that phase-based distance measurement using a narrowbandcarrier signal cannot provide accurate estimate ofdistance in a heavy multipath environment.

Wideband Signals and Superresolution Tech-niques — The direct-sequence spread-spectrum(DSSS) wideband signal has been used in rangingsystems for many years [1]. In such a system, a sig-nal coded by a known pseudo-noise (PN) sequenceis transmitted by a transmitter. Then a receivercross-correlates received signal with a locally gen-erated PN sequence using a sliding correlator or amatched filter [7, 9]. The distance between thetransmitter and receiver is determined from thearrival time of the first correlation peak. Becauseof the processing gain of the correlation process atthe receiver, the DSSS ranging systems performmuch better than competing systems in suppress-ing interference from other radio systems operat-ing in the same frequency band. In single-pathradio propagation channels, only disturbed byadditive white Gaussian noise, the Cramer-Raolower bound is commonly used for performanceassessment of cross-correlation-based TOA esti-mation techniques. However, due to the complexi-ty of multipath indoor radio propagation channels,such a bound is not directly applicable to indoorgeolocation systems. Instead, the resolution ofTOA estimation in DSSS ranging systems is rough-ly determined by the base width of the PN correla-tion function, or equivalently the signal bandwidth[7]. For example, if a bandwidth of 200 MHz isused, the absolute distance estimation errors areless than 1.5 m if the DLOS signal is detectable.

Due to the scarcity of the available band-width in practice, in some indoor geolocationapplications, the DSSS ranging systems cannotprovide adequate accuracy. On the other hand,it is always desirable to achieve higher rangingaccuracy using the same bandwidth. Inspired byhigh-resolution spectrum estimation techniques,a number of researchers have studied super-res-olution techniques for time-domain analysis suchas [13]. A frequency-domain superresolutiontechnique can be used to determine the TOAwith high resolution from frequency channelresponse. In practice, discrete samples of fre-quency channel response can be obtained bysweeping the channel at different frequencies[9], by taking advantage of an existing multicarri-er (orthogonal frequency-division multiplexing,OFDM) communication system, or in a DSSSsystem by deconvolving received signal over thefrequency band of high signal-to-noise ratio [13].

To understand the concept of frequency-domain super-resolution technique, we take theFourier transform of Eq. 1 so that the frequencychannel response is obtained as

(3)

If we exchange the role of time and frequencyvariables in Eq. 3, we observe that it becomes theharmonic signal model, which is well known in thespectrum estimation literature. Therefore, allspectrum estimation techniques used for harmonicsignal models can be applied to frequency domainmeasurement data of radio propagation channelto determine the delay of multipath signals.

In order to demonstrate the usefulness of thesuper-resolution technique we compare its per-formance based on measured indoor channelcharacteristics reported in [12] with two othertime delay estimation techniques. The MUSICalgorithm is used as an example of super-resolu-tion techniques. In the first of these, the fre-quency domain channel response is directlyconverted to the time domain using inverseFourier transform (IFT) [9], and then the arrivaltime of the DLOS is detected. The second tech-nique uses the traditional cross-correlation tech-niques with DSSS signals (DSSS/xcorr). Figure3 shows simulation results using the three tech-niques over sample channel measurement data.We observe that the MUSIC algorithm showsmuch higher time domain resolution than theother two and accurately detects the arrival timeof the DLOS path, while the other two fail. Fig-ure 4a presents mean and standard deviation ofranging errors vs. the bandwidth of the systemover channel measurement data in several differ-ent buildings reported in [12]. Figure 4b presentspercentage of measurement locations whereabsolute ranging errors are smaller than 3 m. Ingeneral, the superresolution technique has thebest performance and is preferred, especiallywhen the signal bandwidth is small. It should benoted that while using the superresolution tech-nique and large bandwidth improves statisticalperformance, it couldn’t eliminate large rangingerrors at some locations because of NLOS con-ditions between transmitter and receiver. Thisneeds to be dealt with in the positioning processto achieve high positional accuracy, as presented

H f ekj f

k

Lk

p

( ) .= -

=

-

 a p t2

0

1

� Figure 3. Estimated TOA of the DLOS path and normalized time domainresponses obtained using three different techniques. The vertical dash-dot linedenotes the expected TOA. The x-axis is delay in ns.

10000

0.2

200 300 400 500 600

0.4

0.6

0.8

1IFTDSSS/xcorrMUSIC

IEEE Communications Magazine • February 2002116

in the next section. Using the superresolutiontechnique increases the complexity of systemimplementation, and there are a number ofissues in practical implementation that need tobe further investigated. More details of thisstudy will be available in a separate publication.

The Ultra Wideband Approach — As men-tioned before, signal bandwidth is one of the keyfactors that affect TOA estimation accuracy inmultipath propagation environments. The largerthe bandwidth, the higher the ranging accuracy.Ultra wideband (UWB) systems, which exploitbandwidths in excess of 1 GHz, have attractedconsiderable attention as a means of measuringaccurate TOA for indoor geolocation applications[6]. Due to the high attenuation associated withthe use of a high-frequency carrier, the frequencyband considered for a UWB system is typicallyfocused on 2–3 GHz unlicensed. With results ofpropagation measurement in a typical modernoffice building, it has been shown that the UWBsignal does not suffer multipath fading [14], whichis desirable for accurate TOA estimation in indoorareas. The actual deployment of UWB systems inthe United States is subject to FCC approval,which was due in late 2001. The main concern ofthe FCC authorities is the interference of UWBdevices with, among other licensed services, theGPS systems that operate at approximately the 1.5GHz frequency band. Similar to the spread spec-trum signals, the UWB signal has a low, flat, andnoise-like power spectrum. But given the weaksatellite signals that must be processed by GPSreceivers, the noise-like UWB signal is still harm-ful for GPS systems in close vicinity. A significantamount of research work is underway to assess theeffect of UWB interference on GPS receivers.

POSITIONING ALGORITHMSAs discussed earlier, the measurement accuracy oflocation metrics in indoor areas depends on loca-tion sensing technologies and site-specific indoor

radio propagation conditions. Due to imperfectimplementation of location sensing techniques,lack of bandwidth, and the complexity of the mul-tipath indoor radio propagation channel amongothers, there are always varying errors associatedwith measurements of location metrics. To achievehigh positional accuracy when the measurementsof location metrics are unreliable, the errorsencountered in the measurement process have tobe mitigated in the positioning process. In the nexttwo subsections we discuss the traditional position-ing algorithms used with reliable measurements oflocation metrics and more intelligent patternrecognition techniques that can be used toimprove the positioning performance when themeasurements of location metrics are unreliable.

TRADITIONAL TECHNIQUESIn the indoor radio channel, it is difficult toaccurately measure AOA, POA and RSS so thatmost of the independent indoor positioning sys-tems mainly use TOA based techniques. Withreliable TOA-based distance measurements, sim-ple geometrical triangulation methods can beused to find the location of the MT [2, 4]. Dueto estimation errors of distances at RP receiverscaused by inaccurate TOA measurement, thegeometrical triangulation technique can onlyprovide a region of uncertainty, instead of a sin-gle position fix, for estimated location of theMT. To obtain an estimate of location coordi-nates in the presence of measurement errors oflocation metrics, a variety of direct and iterativestatistical positioning algorithms have beendeveloped to solve the problem by formulating itinto a set of nonlinear equations [2].

In some indoor geolocation applications, thepurpose of positioning systems is to provide avisualization of possible mobile locations insteadof an estimate of location coordinates [7]. Onthe other hand, positional accuracy is not con-stant across the area of coverage, and poorgeometry of relative position of MT and RP canlead to high geometric dilution of precision [15].

� Figure 4. Simulation results: a) mean of ranging errors in meter vs. bandwidth in MHz using three different TOA estimation tech-niques; the vertical line corresponds to one standard deviation; b) percentage of measurement locations where absolute ranging errorsare smaller than 3 m vs. bandwidth in MHz.

(a)

200-4

-2

16

18040 60 80 100 120 140 160

0

2

4

6

8

10

12

14

(b)

2000

10

90

18040 60 80 100 120 140 160

20

30

40

50

60

70

80IFTDSSS/xcorrMUSIC

IFTDSSS/xcorrMUSIC

IEEE Communications Magazine • February 2002 117

The output of statistical methods is an estimateof mobile location coordinates, and the changesof the shape of the region of uncertainty are notrevealed by this method. When the region ofuncertainty information as well as the estimateof location is needed, both geometric and statis-tical triangulation algorithms are used [15].

For traditional outdoor geolocation, intelligenttechniques, such as Kalman filter-based tech-niques for tracking and fusion of multiple metrics,are normally used to improve positioning perfor-mance [1]. In essence, these techniques are readi-ly applicable to indoor geolocation systems.However, the indoor application environment hassome unique features, discussed in the next sec-tion, which make the traditional positioning algo-rithms less attractive. On the other hand, theseunique features of indoor applications enable thedesign of intelligent positioning algorithms thatcan significantly improve the positioning perfor-mance in indoor areas.

PATTERN RECOGNITION TECHNIQUESFor indoor geolocation applications, the servicearea is restricted to inside and the close vicinity ofa building, and nowadays the building floor plan isnormally accessible as an electronic document.The availability of electronic building floor plans isone of the features of indoor applications that canbe exploited in positioning algorithms. For exam-ple, while tracking an MT in a building, with theaid of a building floor plan situations involvingcrossing walls or jumping through floors can easilybe identified and eliminated. Another unique fea-ture of indoor applications is that the size of thecoverage area is much smaller than outdoor appli-cations. This makes it possible to conduct compre-hensive planning of the placement of sensors.Careful planning of a sensor network can signifi-cantly reduce measurement errors of location met-rics caused by NLOS propagation. The structuralinformation of the sensor network can also beemployed in intelligent positioning algorithms sim-ilar to the use of building floor plans. The smallcoverage of the system also makes it possible toconveniently conduct extensive premeasurementin the areas of interest. As a result, the premea-surement-based location pattern recognition (alsocalled location fingerprinting) technique is gainingincreasing attention for indoor applications [8].On the other hand, in most indoor applications,such as finding needed equipment or locatingpatients in critical condition, the MT is used in aquasi-stationary way. For these situations, patternrecognition work better than traditional triangula-tion techniques and Kalman filter-based trackingtechniques.

The basic operation of pattern recognitionpositioning algorithms is simple. Each buildinghas unique signal propagation characteristics;each spot in a building would have a unique sig-nature in terms of RSS, TOA, and/or AOA,observed from different sensors in the building. Apattern recognition system determines the uniquepattern features (i.e., the location signature) ofthe area of interest in a training process, and thenthis knowledge is used to develop rules for recog-nition. The challenge for such algorithms is to dis-tinguish locations with similar signatures. To buildthe signature database, a terminal is carried

through the service area transmitting signals to amonitoring site through all location sensing ele-ments. The service area is divided into nonover-lapping zones or grids, and the algorithm analyzesthe received signal patterns and compiles a uniquesignature for each zone.

For quasi-stationary applications, the simplestway of pattern recognition is the nearest-neighbormethod. In this method the Euclidean distancemeasure is calculated between the measured met-rics, RSS, TOA, and/or AOA, and all entities inthe signature database. The location estimate isdetermined to be the one associated with theminimum Euclidean distance [8]. A simple experi-ment has been conducted to demonstrate the use-fulness of this technique. Figure 5 presents apartial layout of the Telecommunications Labora-tory (TLab) and the Center for Wireless Commu-nications (CWC) at the University of Oulu,Finland. The locations of four 802.11b accesspoints (APs) and 31 measurement locations alonga long corridor, with about 2 m separationbetween adjacent points, are illustrated in the fig-ure. An MT is carried along the corridor, and theRSS is measured at each location. Figure 6 showsthe measured RSS at all four APs as the terminaltravels from the right corner close to AP-I to theend of the vertical corridor after AP-IV. Then thenearest-neighbor pattern recognition method isapplied to the measurement data. In this experi-ment the standard deviation of the positioningerror was 2.4 m, and at about 80 percent locationsthe positional error was less than 3 m. Similarresults in a different building are available in [8].

When the area of coverage becomes large anda large number of sensors are involved, the size ofthe location signature database increases dramati-cally, which makes the use of simple nearest-neigh-bor pattern recognition computationallycumbersome. More complex algorithms, includingfuzzy logic, neural network, subspace techniques,and hidden Markov model-based techniquesamong others, are being investigated to reduceoverall computational complexity and improve per-formance. When the 3G systems using spreadspectrum signals and RAKE receivers areemployed for indoor geolocation, it is possible to

� Figure 5. Partial layout of the TLab and CWC, University of Oulu, as well asthe locations of the four 802.11b APs and the measurement points.

AP I

AP II

AP III

AP IV

IEEE Communications Magazine • February 2002118

use the measured time and signal strength of allfingers in place of RSS to improve the positioningperformance.

CONCLUSIONSIndoor geolocation is an emerging technologythat needs a scientific foundation. To providesuch a foundation we need to characterize theradio propagation features that impact the per-formance of the indoor geolocation systems. Twoclasses of indoor geolocation systems are emerg-ing. The first class has its own infrastructure, usesreliable TOA measurement using wideband,superresolution, or UWB location sensingapproaches, and employs triangulation tech-niques for positioning. The second class uses theexisting infrastructure of a wireless system (awireless LAN or cellular system), more unreli-able metrics, premeasurement data, and patternrecognition algorithms. The challenge for TOA-based systems is to develop a signaling systemand infrastructure that is inexpensive to designand deploy, complies with frequency regulations,and provides a comprehensive coverage for accu-rate ranging. Even though building and updatingthe signature database are much easier in indoorenvironments than in wide urban areas, the majordrawback of pattern recognition techniques stilllies in substantial efforts needed in generationand maintenance of the signature database inview of the fact that the working environmentchanges constantly. In general, both techniquesdemonstrate promising positioning performancefor the emerging indoor geolocation applications.

ACKNOWLEDGMENTSThe authors would like to thank DARPA, Nokia,Finnish Air Force, TEKES, and NSF for supportof different parts of our research in this area. Wealso thank members of the CWINS at WPI, andthe TLab and CWC at the University of Oulu,who have contributed to our technical under-standing of this field. In particular we thank Dr.P. Krishnamurthy, Dr. J. Beneat, M. Marku, Dr.

R. Tingley, Dr. M.H. Hassan-Ali, and Dr. J.Matthews of WPI, Dr. P. Misra of MIT LincolnLaboratories, P. Creamer and J. Pizano ofTASC/Litton, and Dr. M. Latva-aho, M. Yliantti-la, and R. S. Chana of the University of Oulu.

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BIOGRAPHYKAVEH PAHLAVAN [F] (kaveh@wpi.edu) is a professor of ECEand CS, and director of the CWINS Laboratory at WorcesterPolytechnic Institute, Worcester, Massachusetts. He is alsoa visiting professor of TLab and CWC, University of Oulu,Finland. He is the principal author of Wireless InformationNetworks, John Wiley & Sons, 1995 (with A. Levesques)and Principles of Wireless Networks — A Unified Approach,Prentice Hall, 2002 (with P. Krishnamurthy). He has pub-lished numerous papers, served as a consultant to a num-ber of companies, and sits on the boards of a fewcompanies. He is editor-in-chief of IJWIN, the founder ofthe IEEE Workshop on Wireless LANs, and a co-founder ofthe IEEE PIMRC conference.

XINRONG LI (xinrong@wpi.edu) received his B.E. and M.E.from the University of Science and Technology of China,Hefei, China, and the National University of Singapore in1995 and 1999, respectively. He is pursuing his Ph.D.degree in wireless communications and networks at WPI.His recent research has been focused on indoor geoloca-tion and wireless networks.

JUHA-PEKKA MAKELA (juha-pekka.makela@ee.oulu.fi) receivedhis M.S. degree from the University of Oulu, Finland, in1997. He is working as a research scientist at CWC, Univer-sity of Oulu, and is pursuing his Ph.D. degree in the fieldof mobility management and geolocation applications for4G wireless networks. His research interests include intelli-gent handoff techniques, ad hoc networks, and mobility-related issues in IP-based networks.

� Figure 6. Measured RSS in dBm at the four APs.

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