Research Article Method for Improving Indoor Positioning ...downloads.hindawi.com/journals/misy/2016/2369103.pdf · Research Article Method for Improving Indoor Positioning Accuracy

Research ArticleMethod for Improving Indoor Positioning Accuracy UsingExtended Kalman Filter

Seoung-Hyeon Lee,1 Il-Kwan Lim,2 and Jae-Kwang Lee2

1Cyber-Physical System Security Research Section, Electronics and Telecommunications Research Institute, 218 Gajeong-ro,Yuseoung-gu, Daejeon 34129, Republic of Korea2Department of Computer Engineering, College of Engineering, Hannam University, 70 Hannam-ro, Daedeok-gu,Daejeon 34430, Republic of Korea

Correspondence should be addressed to Jae-Kwang Lee; [email protected]

Received 4 March 2016; Revised 24 May 2016; Accepted 14 June 2016

Academic Editor: Sergio F. Ochoa

Copyright © 2016 Seoung-Hyeon Lee et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Beacons using bluetooth low-energy (BLE) technology have emerged as a new paradigm of indoor positioning service (IPS)because of their advantages such as low power consumption, miniaturization, wide signal range, and low cost. However, the beaconperformance is poor in terms of the indoor positioning accuracy because of noise,motion, and fading, all ofwhich are characteristicsof a bluetooth signal and depend on the installation location. Therefore, it is necessary to improve the accuracy of beacon-basedindoor positioning technology by fusing it with existing indoor positioning technology, which usesWi-Fi, ZigBee, and so forth.Thisstudy proposes a beacon-based indoor positioning method using an extended Kalman filter that recursively processes input dataincluding noise. After defining the movement of a smartphone on a flat two-dimensional surface, it was assumed that the beaconsignal is nonlinear.Then, the standard deviation and properties of the beacon signal were analyzed. According to the analysis results,an extended Kalman filter was designed and the accuracy of the smartphone’s indoor position was analyzed through simulationsand tests. The proposed technique achieved good indoor positioning accuracy, with errors of 0.26m and 0.28m from the average𝑥- and 𝑦-coordinates, respectively, based solely on the beacon signal.

1. Introduction

Location determination technology in smartphonesequipped with global positioning system (GPS) and Wi-Fihas not only triggered a shift in the paradigm of location-based services (LBS) but also contributed significantlytoward the further development of such services, includingnavigation and logistics [1, 2]. IPS are systems that canprovide all types of LBS—usually provided outdoors—inan indoor area. It is important for IPS to map a user’sindoor positioning information on indoor maps in order toprovide the services required by the user. Indoor positioningtechniques previously adopted by IPS include the 𝐾-nearestneighbor, Bayesian, and triangulation methods, whichare based on wireless technologies such as Wi-Fi, radiofrequency identification (RFID), and ZigBee [3–6]. Recentstudies have focused on an ultrawideband-based indoorpositioning technology [7, 8].

In recent years, beacons have not only contributed towardthe growth of mobile LBS but also emerged as a newparadigm of IPS. Beacons are BLE-based short-distancecommunication technologies. Their advantages include lowpower consumption, miniaturization, wide signal range, andlow cost. Moreover, when communicating with devices as faraway as 70m, the accuracy of BLE is sufficiently high fordistinguishing each device with a resolution of 5–10 cm [9–11].

In 2013, Apple introduced iBeacon�, and in the fol-lowing year, iBeacon began to be implemented in areaswhere various types of information could be provided onthe basis of user position, such as information regardingrestaurants, airports, art galleries, and other indoor areas.Figure 1 shows an example of a service using iBeacon [12]. AniBeacon transmitter is installed at a specific location withina store; although it does not attempt to guide customersto a specific location, it provides them with an automated

Hindawi Publishing CorporationMobile Information SystemsVolume 2016, Article ID 2369103, 15 pageshttp://dx.doi.org/10.1155/2016/2369103

2 Mobile Information Systems

Shoppinginformationadvertising

Proximitymarketing

(connect app)

Nearest signal:product information

(price, discountcoupon)

Productinformationadvertising

Contactlesselectronicpayment

Figure 1: Service case with iBeacon.

shopping service by determining their positions throughoutthe shopping process. This extends from the moment theyenter the store to when they request for information about aproduct (including price) and eventually pay for the product.

As shown in Figure 1, the iBeacon transmitter period-ically broadcasts advertising packets, including the deviceidentification (ID), area ID, and signal strength, and eachsmartphone that receives this information calculates thedistance based on the location at which the iBeacon trans-mitter is installed and the signal strength. Then, the positionof the smartphone is located through either control pointpositioning or multilateral positioning, thus providing thecustomer with the necessary service and information.

Although the BLE-based iBeacon has many advantages,its performance is poor in terms of the indoor positioningaccuracy of a smartphone, and it is difficult to estimate thedistance accurately using only the strength of the signaltransmitted from the iBeacon transmitter to the smartphone.These issues are attributable to noise, movement, and fading[13, 14], all of which are characteristic of a bluetooth signaland depend on the installation location. Therefore, althoughit is possible to achieve indoor positioning depending solelyon the iBeacon signal, the indoor positioning accuracyneeds to be improved by fusing the iBeacon approach withanother indoor positioning approach based on technologiessuch as Wi-Fi. However, this would require multiple sensorswith different models to be installed in an indoor space,which would introduce additional issues such as higher costsassociated with initial construction and maintenance.

Accordingly, this study proposes a technique for improv-ing the indoor positioning accuracy of smartphones usingonly the iBeacon signal. Because the beacon is installed in anopen space, themovement of a smartphone indoors is definedas movement on a flat two-dimensional surface. In addition,

because the beacon’s received signal strength indicator-(RSSI-) based distance measurement and the smartphone’slocation on the two-dimensional surface have a nonlinearrelationship, an extended Kalman filter is used. Once thestandard deviation and properties of the beacon signal areanalyzed, the extended Kalman filter is designed on the basisof this analysis. Finally, the indoor positioning accuracy of thesmartphone is analyzed by performing simulations and tests.

The remainder of this study is organized as follows.Section 2 briefly reviews previous studies on iBeacon and theextended Kalman filter, which are applied to the techniqueproposed herein. Section 3 explains the design and imple-mentation of the extended Kalman filter, which improves theiBeacon-based indoor positioning accuracy. In addition, thecomponents of the indoor positioning system and the testingenvironment are described. Section 4 presents and discussesthe results of both the iBeacon signal analysis and the indoorpositioning test. Finally, Section 5 states our conclusions andbriefly explores the scope for future work.

2. Related Work

2.1. iBeacon. Having introduced the BLE-based iBeacon inJune 2013, Apple subsequently launched a variety of servicesthat use iBeacon. BLE, which was introduced in June 2010,is equipped with low-energy technology and is commonlyreferred to as bluetooth smart. BLEminimizes the time spentsending/receiving data by reducing the duty cycle to severalmilliseconds, and it maximizes the sleep mode to reducepower consumption [10]. Because iBeacon is based on astandard BLE technology, it can be supported by any device.Therefore, it is extendible even to a non-iOS device as long asthe device is compliant with the standard specifications [15].

iBeacon periodically broadcasts only a few signals,including the universally unique identifier (UUID), majorvalue, minor value, and transmissions (TX) power, to identifythe beacon and calculate the distance, as shown in Figure 2.Thus, all indoor LBS are processed on smartphones andservice servers.

In the case of the Estimote Beacon (Estimote Beaconsare small wireless sensors based on BLE; they broadcasttiny radio signals that can be received and interpreted bysmartphones, thereby facilitating microlocation and con-textual awareness; Estimote Beacons are tiny computers,each having a powerful Advanced RISC Machine (ARM)processor, memory, and bluetooth smart module; EstimoteBeacons are certified to be compatible with iBeacon), whichsupports iBeacon, the signal transmission cycle and outputcan be easily modified using a specialized tool. Changingthe signal transmission cycle and output will alter the dis-tance measured between the iBeacon transmitter and thesmartphone [16]. However, modifying the signal output doesnot guarantee improved accuracy of the measured distance.Therefore, the measurement derived from the iBeacon signalhas to be interpreted differently depending on the distance,as summarized in Table 1 [15].

Nevertheless, the definitions stated in Table 1 do notmeanthat the distance cannot be directly measured using iBeacon.In fact, it means that, in the case of distances estimated using

Mobile Information Systems 3

Preamble(1 byte)

Accessaddress(4 bytes)

PDU(2–39 bytes)

CRC(3 bytes)

BLE advertisement packet format

Advertising channel PDU

Header(2 bytes)

MACaddress(6 bytes)

Data(0–31 bytes)

iBeaconprefix

(9 bytes)

ProximityUDDI

(16 bytes)Major

(2 bytes)TX

power(1 byte)

Minor(2 bytes)

iBeacon data packet format

Figure 2: iBeacon packet format.

Table 1: Four proximity states of iBeacon.

Glossary DescriptionAccuracy A distance value in terms of meters

Proximity state (estimates of thedistance between the iBeacontransmitter and the smartphone thatappears in the CLBeacon class)

(i) Immediate: distance between 0 and 1m; high reliability.(ii) Near: distance between 1 and 3m; trusted distance.(iii) Far: distance greater than 3m. The measured distance is not the actual distance to the iBeacontransmitter.(iv) Unknown: iBeacon signal is not received.Measurement reference value: the RSSI values (−59 dBm) received at a distance of 1m.

iBeacon, a relative revision should be considered from theperspective of accuracy.

2.2. Extended Kalman Filter. In 1960, Kalman proposed theKalman filter as a recursive filter for determining the randomvalues of linear and nonlinear systems containing noise [17].Using its algorithm to track/predict and thus revise the past,present, and future states, the Kalman filter can plot a system’strajectory,making it suitable for application tomodel systemssuch as radars, which anticipate and estimate the next state[18–21].

Figure 3(a) shows the basic algorithmof the Kalman filter,which takes a measurement value (𝑍𝑘) as input and outputs apriori state estimate (��𝑘) of the next state. It is a repetition ofprediction and correction processes [22].

(i) The prediction process inputs a former a posterioristate estimate (��𝑘−1) and a posteriori error covariance(𝑃𝑘−1), calculating a posteriori state estimate and apriori estimate error covariance (��

𝑘, 𝑃𝑘) as the end

result.These values are used in the correction process.The system model variables used in the predictionprocess are the state transition matrix (𝐴) and theprocess noise covariance (𝑄).

(ii) The output of the correction process is a posterioristate estimate (��𝑘) and a posteriori estimate errorcovariance (𝑃𝑘). The values used for the input arenot only the a posteriori state estimate and the apriori estimate error covariance (��

𝑘, 𝑃𝑘) from the

prediction process but also the measurement (𝑍𝑘).The system model variables used in the correctionprocess are the measurement prediction matrix (𝐻)and the measurement error covariance matrix (𝑅).

The Kalman filter allows accurate forecasting by usingthe measured value of the current state as well as thevalue reflecting the prediction from the current state. It isfundamentally based on a linear system. Hence, when itis applied to a nonlinear system, a section of the wholeis linearized before the algorithm is applied, rendering itproblematic, as a meaningful result can only be drawn fromthe property of the designated section and not the whole.

An extended Kalman filter allows the nonlinear relation-ship between the state value and the dynamic model or themeasurement model in the Kalman filter to be linearized andused with respect to the nominal point [23, 24]. It is used asa de facto standard in navigation, GPS, and other nonlinearstate estimations [25]. The Kalman filter has a measurementmodel and a dynamic model, whereas the extended Kalmanfilter shapes either model or both models into a nonlinearmodel.

Figure 3(b) shows the basic algorithm of the extendedKalman filter, which is a repetition of the prediction andcorrection processes, as with the Kalman filter. However, itdiffers from the Kalman filter in that the state value of 𝑥𝑘does not use the coefficient separately but uses the nonlineardynamicmodel ormeasurementmodel as it is, such as 𝑥𝑘+1 =𝑓(𝑥𝑘) + 𝑤𝑘 or 𝑧𝑘 = ℎ(𝑥𝑘) + V𝑘 [22].

3. Design and Implementation of ExtendedKalman Filter

3.1. Design. This study employs an iBeacon transmitter asa control point for measuring the indoor position of asmartphone. Because the BLE technology applied to iBeaconcan only be used in an open space owing to the nature of radio


Initial estimates forxk−1 and Pk−1

Time update (predict)(1) Project the state ahead:

(2) Project the error covariance ahead:Pk= APk−1A

T + Q

Measurement update (correct)

(3) Update the error covariance:Pk = (I − KkH)P

k

MeasurementZk

State estimatexk

= Axk−1 + Bukxk

(1) Compute the Kalman gain:Kk = P

kHT(HP

kHT + R)

−1

(2) Update estimate with measurement:xk = x

k+ Kk(zk − Hx

k)

(a) Kalman filter


Time update (predict)

(2) Project the error covariance ahead:Pk= AkPk−1Ak

T + WkQk−1WkT

Measurement update (correct)(1) Compute the Kalman gain:

(2) Update estimate with measurement:

(3) Update the error covariance:Pk = (I − KkHk)P

k

MeasurementZk

State estimatexk

(1) Project the state ahead:= f(xk−1, uk, 0)x

k

Kk = PkHk

T(HkPkHk

T + VkRkVkT)−1

xk = xk+ Kk(zk − h(x

k, 0))

(b) Extended Kalman filter

Figure 3: Comparison between Kalman filter and extended Kalman filter (the state transition matrix (𝐴) has the following differences: (a) itis not a Jacobian matrix with a linear property; (b) its value is defined in accordance with a Jacobian matrix having nonlinear properties).

signals, separate iBeacon transmitters have to be installed perfloor and per space. Therefore, when setting a space mapusing the iBeacon transmitters, registration/management hasto be performed according to the iBeacon ID value per floorand per space.

As discussed later (Section 4.1), iBeacon signals undergofading. Therefore, the accuracy is low when measuringthe distance using only RSSI measurements. In order toimprove the distance measurement accuracy, sampling must

be performed to eliminate the iBeacon signal noise. Asmartphone requires a sampling time of 20 s.

TheKalman filter processes the input data including noiserecursively. Further, it provides higher accuracy because itis based on a series of measurements observed over timerather than a single measurement alone, thereby facilitatingstatistical prediction of the current state. The Kalman filtercan estimate the current state from the previous state usingthe linear property. However, if the signal is nonlinear, as in



Time update (predict)

(1) Project the state ahead:xk= Axk−1

(2) Project the error covariance ahead:Pk= APk−1A

T + Q

Measurement update (correct)

(1) Compute the Kalman gain:

(2) Update estimate with measurement:

(3) Update the error covariance:Pk = (I − KkHk)P

k

MeasurementZk

State estimatexk

Kk = PkHk

T(HkPkHkT + Rk)−1

xk = xk+ Kk(zk − h(x

k))

Figure 4: Design of extended Kalman filter.

the case of iBeacon signal analysis, the Kalman filter cannotbe applied. In this study, considering the nonlinear propertyof iBeacon signals, the extended Kalman filter is employed.

In this study, the extended Kalman filter is designed asfollows:

(i) In the measurement model, iBeacon’s RSSI-baseddistancemeasurement shares a nonlinear relationshipwith the state value of the smartphone position.

(ii) In the dynamicmodel, movements of the smartphoneposition have the appearance of a uniform motion-based linear motion model.

In the measurement model, the RSSI value of the iBeaconsignal is assumed to be a measurement (𝑍), and this is usedwhen calculating the distance. When the distance is deter-mined using the RSSI value, the decrease in the receptionrate per distance can be inconsistent; hence, the nature of themeasurement (𝑍) is deemed nonlinear. The dynamic modelis primarily used when calculating the trajectory of missiles,artificial satellites, and so forth. Factors that cause changesin 𝑥𝑘 include the location, velocity, and acceleration, and theresistance it faces varies according to the object’s shape andaltitude, meaning that there is a quality of dynamic change.The component of the existing algorithm of the Kalman filterthat considers the dynamic quality is the weight (𝑊).

The extended Kalman filter designed for this studyassumes that smartphones move linearly on a flat surfacewithout changes in height; therefore, the dynamic quality is

equivalent to the characteristic of an object moving on a two-dimensional surface.

Figure 4 shows the algorithm of the extended Kalmanfilter that was newly designed to reflect the characteristicsof the iBeacon signal as well as the smartphone’s movementdescribed above.

3.2. Implementation. The indoor positioning function of asmartphone using the newly designed extended Kalman filterbased on iBeacon (Section 3.1) is shown in Figure 5.

(1) The smartphone calculates the distance between theiBeacon transmitter and itself on the basis of theRSSI of the received iBeacon signal. It sets the RSSIvalue received from 1m away as the standard ofmeasurement (−59 dBm).

(2) The data structure containing the identifier of theiBeacon signal and the calculated distance are trans-mitted to the IPS module via Wi-Fi (or cellularnetwork).

(3) The IPS module extracts the base station (BS) coordi-nates (𝑥-𝑦) allotted to each iBeacon transmitter andcreates an input structure of the extended Kalmanfilter using the coordinates and distance; “age” isthe total number of iBeacon signals found on thesmartphone.

(4) The location based on the nonlinear distance iscalculated by repeatedly performing the initialization,


Smartphone

Number of BSBS ID,BS position on x-axis,BS position on y-axis

Number of BS = ageBS position = control point location value

Input

Initialization

Yes

No

Prediction

Correction

End cycle?

Output

Matrix

matCopy

matAddition

matSubtraction

matMultiplication

matInverse

· · ·

matTranspose

Core function

Age > 0

Yes

No

BS position→

Observed information = ID and distance= iBeacon indexNumber of observations

Figure 5: Logic configured for the implementation of the extended Kalman filter.

prediction, and correction processes. The matrixfunction is a two-dimensional covariance matrix.

Figure 6 shows the detailed flow of the initialization,prediction, and correction processes of the “Core Function”in Figure 5.

(1) Initialization. The BS value is received by the smart-phone and the “age” is confirmed. The initializationprocess is conducted when the number of recursionsis zero. The initialization process involves calculatingthe 𝑥 and 𝑦 values using the extracted BS value, andit initializes the a priori state estimate value. Oncethis process is completed, the number of recursionsincreases and the variable value is delivered for thenext process.

(2) Prediction. Once the initial value of the initializationprocess or the a priori state estimate value based onthe recursion result of the extended Kalman filteris provided as input, the local value is initializedto estimate the a posteriori state estimate value. In

addition, the time and a posteriori state estimate valueare computed with a fixed process noise covariance.The state transition matrix is set as noise for thecovariance matrix. Then, a posteriori state estimateis extracted by calculating the state error covariance,and it is delivered to the correction process.

(3) Correction. The local value is initialized, the BS valueis extracted, and the state vector is transmitted. Then,the measurement prediction matrix and residualvalue are calculated. Subsequently, the Kalman gain iscomputed and the state vector correction and trans-mission value are estimated. Next, the correct valueand error covariance are calculated, which concludesthe correction process, after which the “age” value isincreased.

(4) Steps (2) and (3) are repeated until smartphonelocation tracking is completed.

3.3. Configuration of Base Station and Indoor PositioningSystem. Figure 7 shows a smartphone location tracking


Extended Kalman filter loop

Initi

aliz

atio

n Pred

ictio

n

Cor

rect

ion

Extracting the locationof BS

State vector transition

Measure prediction matrix (H)calculation

Measurement error covariancematrix (R) calculation

Measurement residual (dZ)calculation

Update time calculation(multiplier)

State transition matrixinitialization

A

Process noise covariancecalculation

Q

Extracting the locationof BS

Location coordinatesinitialization

Measurement error covariancematrix initialization

R

Estimate error covariance matrixinitialization

P

Variable initialization Variable initializationVariable initialization

k

Input:iBeacon signal

End cycle?

Output:location information

Yes

No

No

Yes

A posteriori error covariancecalculation

Pk= APk−1A

T + Q

Age > 0

age++Number of BS++

age++Number of BS++

Update time calculation

Ts2 = Ts2Ts = 1 (update period)

State predicationxk= Axk−1

Kalman gain calculationKk = P

kHk

T(HkPkHkT + Rk)−1

Position estimate calculationxk = x

k+ Kk(zk − h(x

k))

Error rate calculationPk = (I − KkHk)P

k

k + 1

Centroid calculationx = 1.0 ∗ x/range/range sumy = 1.0 ∗ y/range/range sum

Figure 6: Smartphone positioning process using extended Kalman filter.

system that uses iBeacon. It consists of a smartphone thatestimates and transmits the distance between the BS and itselfon the basis of the RSSI from the compiled iBeacon signalsand a server system that calculates the smartphone’s locationon the basis of the BS coordinates and estimated distance.This system was developed in an environment described inTable 2.

iBeacon’s maximum transmission distance is defined as90mand the standard transmission cycle is defined as 100ms.However, the cycle can be programmed to operate slower,that is, at 900ms. The Estimote Beacon, which was usedin this study, has a transmission cycle of 200ms and amaximum transmission distance of 70m. Figure 8 shows theconfiguration of a BS using Estimote Beacons. Each grid is0.5m long and six iBeacons are installed.

The BS was configured on the basis of the signal property(see Section 4.1). The iBeacons were defined to be locatedwithin 5m according to the distance standard deviation ofthe iBeacon signals received at the smartphone.

4. Analysis

4.1. Signal Property. In this study, we analyzed the signalproperty of the iBeacon’s measured distance, which was usedin experiments before we performed the indoor position-ing of smartphones using the extended Kalman filter. Thedistance was estimated using the RSSI value included inthe iBeacon signal. The reference points for the measureddistance were 1m, 3m, and 5m; each was measured 1,500times, after which their standard deviation and average werecalculated. Allowing for the signal’s path loss, the distancemeasurement using the iBeacon RSSI can be calculated asfollows:

PL = 𝑃TX − 𝑃RX, (1)

PL (𝑑) = PL (𝑑0) + 10 ⋅ 𝑛 ⋅ log10 (𝑑

𝑑0

) + 𝑋𝑔, (2)


Signalgathering

Sending informationWi-Fi

or cellular

UUIDMajor valueMinor value

UUIDMajor valueMinor value

App

Signal Send

iBeacon SDK

Server

CentOS

Informationprocessing

Smartphonepositioning

Smartphonetrace Release 2

Oracle 11 g

UU

ID

Maj

or

Min

or

RSSI

Dist

ance

And

so fo

rth

Figure 7: Configuration of smartphone indoor positioning system based on iBeacon.

Smartphone(x, y)

BS1(0, 0)

BS3(0, 6)

BS5(0, 12)

BS6(11, 12)

BS4(11, 6)

BS2(11, 0)

00

1 2 3 4 5 6 7 8 9 10 11

1

2

3

4

5

6

7

8

9

10

11

12

Figure 8: Configuration of BS. iBeacon Specification [26]: (i)Model: Estimotemodel REV.D3.4 Radio Beacon. (ii) CPU: 32-bit ARM�Cortex-M0. (iii) Frequency range: 2400–2483.5MHz. (iv) TX-receptions (RX) channel separation: 2MHz. (v) Sensitivity: −93 dBm. (vi) Range: upto 70m. (vii) Upgrade: over-the-air firmware and security upgrades.

Table 2: Development environment.

Division Item SpecificationsiBeacon Estimote Beacon ×6 ARM Cortex-M0, BLE, motion and temperature sensorsSmartphone H/W iPhone 5S

App development platform

OS OS X version 10.10.5 (Yosemite)Tool Xcode version 7.1.1

Language Objective-CH/W MacBook Pro, 3.1 GHz Intel Core i7, 16GB DDR3 RAM

Server

OS CentOS release 6.5 (Final)Kernel 64 bit

Language C, XMLDB Oracle Database Express Edition 11g Release 2


1 36 71 106

141

176

211

246

281

316

351

386

421

456

491

526

561

596

631

666

701

736

771

806

841

876

911

946

981

1016

1051

1086

1121

1156

1191

1226

1261

1296

1331

1366

1401

1436

1471

BS1BS2BS3

BS4BS5BS6

0.000

0.200

0.400

0.600

0.800

1.000

1.200

1.400

Figure 9: Signal property at 1m.

𝑃RX (𝑑) = 𝑃RX (𝑑0) − 10 ⋅ 𝑛 ⋅ log10 (𝑑

𝑑0

) ,

(derived from (2)) ,(3)

𝑑 = 𝑑0 ⋅ 10(𝑃RX(𝑑0)−𝑃RX(𝑑))/10𝑛

,

(derived from (3)) ,(4)

where PL is total path loss, 𝑃TX/𝑃RX are transmitted andreceived signal strength, respectively, 𝑑 is measured distance,𝑑0 is standard distance (define: 1m),𝑋𝑔 is standard deviation(define: 0), and 𝑛 is path loss exponent.

After substituting 𝑃RX(𝑑0) with −59 dBm, the signalstrength for the 1m reference point described in the iBeacondevelopment document, (1), (2), (3), and (4) were used tocalculate the relative distance measurement per RSSI signalstrength.

Figure 9 shows measurements of the distance to theiBeacon transmitter from a distance of 1m. The average ofthe six measurements to the iBeacon transmitter was 0.6m,which had an error of 0.34m. Figure 10 shows measurementsof the distance to the iBeacon transmitter from a distanceof 3m. The average of the six measurements to the iBeacontransmitter was 1.39m, which had an error of 1.61m. Fig-ure 11 shows measurements of the distance to the iBeacontransmitter from a distance of 5m. The average of the sixmeasurements to the iBeacon transmitter was 2.21m, whichhad an error of 2.79m.

Thus, the distance measurements from the 1m, 3m, and5m points resulted in errors of 0.34–2.79m from the realdistance. Based on this result, it can be confirmed that

while it is possible to measure the distance using only theiBeacon’s RSSI value, an error would exist between the realand calculated distances. This is due to a number of factorsthat cause changes in the received RSSI value, such as thebattery life, experiment environment, space configuration,human movement, and smartphone location and direction.

Figure 12 shows the average measurement per unitdistance. The average was calculated using the distancemeasured on the basis of the RSSI value determined fromdistances of 1m, 2m, 3m, 5m, and 10m. Figure 13 showsthe standard deviation of themeasurements per distance.Thedistance was measured using 1m, 2m, 3m, 5m, and 10m asreference points to verify the accuracy of the “immediate,”“near,” and “far” distances defined in the iBeacon devel-opment document. As can be seen, the deviation was nothigh when the measurement was taken from distances of 1mand 2m, but it increased for distances of 3m and 5m andincreased further for 10m.

4.2. Indoor Positioning Using Extended Kalman Filter. Fromthe analysis of the iBeacon’s signal property (Section 4.1), weconfirmed that there were irregularities and variations in themeasurements of the iBeacon RSSI according to the distanceand the beacon. Because a single distance measurementusing the iBeacon RSSI is not reliable, it is necessary tocreate a measurement noise model for application to anextended Kalman filter based on the result of the signalproperty analysis. Table 3 lists the inclination per section ofthe standard deviation shown in Figure 13.

As can be seen in Table 3, BS6 is not a reliable measure-ment because of the irregular change in the deviation per


1 36 71 106

141

176

211

246

281

316

351

386

421

456

491

526

561

596

631

666

701

736

771

806

841

876

911

946

981

1016

1051

1086

1121

1156

1191

1226

1261

1296

1331

1366

1401

1436

1471

BS1BS2BS3

BS4BS5BS6

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500


1 36 71 106

141

176

211

246

281

316

351

386

421

456

491

526

561

596

631

666

701

736

771

806

841

876

911

946

981

1016

1051

1086

1121

1156

1191

1226

1261

1296

1331

1366

1401

1436

1471

BS1BS2BS3

BS4BS5BS6

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000



Table 3: Distance standard deviation of iBeacon.

Section BS1 BS2 BS3 BS4 BS5 BS61m-2m standard deviation 0.02 0.05 0.02 0.1 0.05 −0.052m-3m standard deviation 0.01 −0.06 0.05 −0.05 0 0.23m–5m standard deviation 0.12 0.1 0.06 0.04 0.24 −0.075m–10m standard deviation 0.23 0.36 0.37 0.34 0.01 0.06Standard deviation 0.09 0.11 0.12 0.11 0.08 0.041m–5m standard deviation 0.05 0.03 0.04 0.03 0.1 0.03

BS1BS2BS3

BS4BS5BS6

00.5

11.5

22.5

33.5

44.5

5

2 3 4 51

Figure 12: Average measurement chart.

BS1BS2BS3

BS4BS5BS6

Standard deviation of measurement noise

2 3 4 510

0.1

0.2

0.3

0.4

0.5

0.6

Figure 13: Standard deviation chart.

section. The deviations of the other iBeacon RSSIs besidesBS6 increased sharply in the section between 5m and 10m.Therefore, in this study, the measurement noise model wasdefined as follows, by reflecting the property of the iBeaconRSSI to apply the model to the algorithm of the extendedKalman filter:

𝜎𝑑 = 𝑘2𝑑,

𝑘 = 0.2, (derived from Table 3) .(5)

The system variables of the extended Kalman filter usingthe results presented above can be defined as follows:

(i) 𝑄 Value (standard deviation of process noise): it isstandard deviation of themotionmodel errors. In thisstudy, a scale factor of 1.0 was applied.

Define

𝑄 = 1.0. (6)

(ii) 𝑅 Value (standard deviation of measurement noise):it is standard deviation of the measurement modelerrors. In this study, the value was derived from (5).

Define

𝜎1

𝜎m

0

0

R = (7)

Next, we discuss the analysis result of the smartphonecoordinates measured in an experiment environment usingthe extended Kalman filter, which reflects the analysis of theiBeacon’s signal property.

Table 4 lists the values of the iBeacon signal that wasmeasured 1,000 times at the 𝑥- and 𝑦-coordinates (2, 5).Table 5 and Figure 14 specify the indoor positioning valueapplied with the extended Kalman filter. The average valuesof the 𝑥- and 𝑦-coordinates were 2.05 and 5.3, that is, errorsof 0.05 and 0.3 from the actual coordinates, and the standarddeviations were 0.16 and 0.13, respectively. Because of thenature of the extended Kalman filter, where the accuracyimproves as the number of recursions increases, the average𝑥- and 𝑦-coordinates were 2.13 and 5.3, respectively, whenthe number of recursions ranged from 1 to 20. Moreover, theaverage coordinates became 2.1 and 5.3 when the number ofrecursions varied from81 to 100, indicating an error reductionof up to 0.03 and 0.04, respectively.

Table 6 lists the values of the iBeacon signal that wasmeasured 1,000 times at the 𝑥- and 𝑦-coordinates (6, 7).Table 7 and Figure 15 specify the indoor positioning valueapplied with the extended Kalman filter. The average values


Table 4: Gathering signals from (2, 5).

Number BS ID BS 𝑥-coordinates

BS 𝑦-coordinates RSSI Distance

1 BS1 0 0 −70 3.572 BS2 11 0 −63 1.563 BS3 0 6 −76 4.974 BS4 11 6 −74 7.735 BS5 0 12 −70 4.816 BS6 11 12 −74 5.697 BS1 0 0 −68 3.508 BS2 11 0 −62 1.539 BS3 0 6 −76 5.0910 BS4 11 6 −74 7.73...

.

.

....

.

.

.

.

.

.

.

.

.

991 BS1 0 0 −70 3.83992 BS2 11 0 −63 1.44993 BS3 0 6 −69 4.5994 BS4 11 6 −74 7.22995 BS5 0 12 −88 5.55996 BS6 11 12 −74 6.08997 BS1 0 0 −68 3.73998 BS2 11 0 −64 1.46999 BS3 0 6 −69 4.391000 BS4 11 6 −76 7.2421

Table 5: Positioning result from (2, 5).

Age 𝑥 𝑦

1 3.38 5.572 2.62 5.383 2.09 4.974 2.16 5.35 2.11 5.386 2.07 5.287 2.14 5.358 2.16 5.199 2.18 5.5410 2.05 5.24⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅

91 2.13 5.2492 2.11 5.493 2.07 5.3994 2.04 5.2595 2.06 5.2796 2.16 5.2997 2.08 5.2398 2.08 5.2899 2.03 5.19100 2.07 5.39

of the 𝑥- and 𝑦-coordinates were 5.24 and 8.2, that is, errorsof−0.76 and 1.2 from the actual coordinates, and the standard

0

1

2

3

4

5

60 0.5 1 1.5 2 2.5 3 3.5 4

Figure 14: Positioning result chart (2, 5).

0

2

4

6

8

10

120 1 2 3 4 5 6 7


deviations were 0.48 and 1.03, respectively.The errors derivedfrom the average coordinates were accumulated, and whenthe number of recursions ranged from 81 to 100, the errorsin the average 𝑥- and 𝑦-coordinates became 1.14 and 0.1,respectively, as listed in Table 7. The coordinates with thelargest error exhibited a higher standard deviation comparedto the other coordinates; thus, it could be concluded that theiBeacon RSSI value was influenced most significantly by theexternal environment.

Table 8 lists the values of the iBeacon signal that wasmeasured 1,000 times at the 𝑥- and 𝑦-coordinates (9, 8).Table 9 and Figure 16 specify the indoor positioning valueappliedwith the extendedKalman filter.The average values ofthe 𝑥- and 𝑦-coordinates were 9.43 and 7.81, that is, errors of0.43 and −0.19 from the actual coordinates, and the standarddeviations were 0.48 and 1.03, respectively. As specified inTable 9, the errors in the 𝑥- and 𝑦-coordinates increased by0.35 and 0.2, respectively, when the number of recursionsranged from 81 to 100.

By combining all the measurements for each coordinate,it could be confirmed that the errors in the average coor-dinates reflecting the total number of recursions were 0.53and 0.56 for the 𝑥- and 𝑦-coordinates, respectively. Thisis due to the nature of the extended Kalman filter, wherethe error margin decreases as the number of recursionsincreases. The average errors for 1 to 20 recursions were 0.33and 0.53, respectively, while the errors reduced to 0.17 and0.48, respectively, when the number of recursions rangedfrom 80 to 100. Considering that each grid was 0.5m long,






.

.

....

.

.

.

.

.

.

.

.

.



Age 𝑥 𝑦

1 5.51 7.362 5.66 7.793 6.07 7.374 6.03 8.225 5.82 6.846 6.04 9.077 5.79 7.078 5.94 9.339 5.93 6.8210 5.65 9.4⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅

91 4.48 8.0692 4.21 8.1493 4.3 8.2894 4.4 8.3795 4.7 8.6196 4.75 8.5197 4.52 8.4298 4.46 8.2399 4.32 8.43100 4.61 8.44





.

.

....

.

.

.

.

.

.

.

.

.



Age 𝑥 𝑦

1 8.11 7.052 9.43 7.893 9.32 8.14 9.27 8.235 10.02 7.996 8.68 8.127 10.12 7.988 9.14 8.029 9.8 8.1910 9.1 8.01⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅

91 9.95 8.1392 9.85 8.1593 9.87 7.9494 9.73 7.6695 10.55 8.3496 7.92 7.9697 10.06 7.5598 9.06 7.8699 9.7 7.57100 9.58 7.5


0

2

4

6

8

100 2 4 6 8 10 12


the smartphone’s indoor positioning accuracy had errors of0.26m and 0.28m for the 𝑥- and 𝑦-coordinates, respectively.

5. Conclusion

Although LBS have been diversified and enhanced throughthe use of bluetooth, Wi-Fi, GPS, and sensor technologies insmartphones,most such services are still used outdoors.Withthe proliferation of construction techniques and informationand communications technologies, there is a growing needto provide LBS indoors in the same way as they have beenpreviously provided outdoors. Facilitating the provision ofLBS in an indoor area requires technologies that can plotindoor maps, identify the positions of users accurately, andanalyze routes efficiently. BLE-based beacons periodicallybroadcast signals containing ID and RSSI, thus playing therole of reference points for locations, as with indoor GPS.Following the launch of iBeacon, Apple applied it to a mobilepayment service that replaced near field communication andto a service that provides information on matches held at thestadiums of the Boston Red Sox, LA Dodgers, and so forth.The key technology of IPS involves the identification of asmartphone location.The existing beacon-based indoor posi-tioning technology has low performance accuracy because ofthe characteristics of bluetooth signals; hence, it is necessaryto enhance the indoor positioning accuracy by combining theexisting approach with a location determination approach,which uses wireless technologies such as Wi-Fi and ZigBee.However, this increases the burden of the installation cost fora number of sensors having different models and leads to anincrease in the development and maintenance costs becauseof the fusion of multilateral positioning technology.

To overcome the abovementioned issues, this study pro-posed a technique for enhancing the indoor positioningaccuracy in a two-dimensional indoor area using only abeacon signal. Once it was assumed that the RSSI signalhas a nonlinear property, the iBeacon measurement wasanalyzed and the standard deviation was used to definethe measurement noise model (𝑅) that reflected the signalproperties by iBeacon and by distance. The defined 𝑅 modelwas applied to the extended Kalman filter that was improvedon the basis of the bluetooth signal property, which resultedin a relatively accurate calculation of the smartphone’s indoor

position. In this study, we were able to achieve improvedaccuracy compared to beacon-based trilateration. However,a limitation of this study is its inability to sufficiently reflectthe smartphone’s movement and the beacon’s signal property.Based on this study, the next step is to conduct a detailed anal-ysis of the smartphone’s movement and the bluetooth signaldeviation to generate a weighted valuemodel for each beaconsignal and its standard deviation model, which will then beapplied to the extended Kalman filter. Furthermore, in thefuture, we aim to develop a model for restricted area accesscontrol using indoor positioning accuracy enhancements.

Competing Interests

The authors declare that they have no competing interests.

Acknowledgments

This work was supported by the Human Resource TrainingProgram for Regional Innovation and Creativity through theMinistry of Education and National Research Foundation ofKorea (NRF-2014H1C1A1073140).

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