WAY: Seamless Positioning Using a Smart Device

Alfonso Bahillo1 • Teodoro Aguilera2 • Fernando J. Alvarez2 •

Asier Perallos1

� Springer Science+Business Media New York 2016

Abstract Smart devices are attractive platforms for researchers to collect data coming

from several sensors due to their small size, low cost, and the fact that they are already

carried routinely by most people. The capability of smart devices to be used as the target of

a positioning system has been already demonstrated in previous works. However, most of

them rely on a single technology, or they are specific to the environment or user. In this

paper we tackle these constraints by presenting a novel seamless positioning system which

fuses the sensors information provided by a portable smart device to perform real time

location without interruption and independently of the environment the user is moving. We

have tested the system with a commercial smart device in an uncalibrated three floor

building and its surroundings fusing the GNSS, WiFi and barometer as frequently used

sensors, and the microphone and the proximity contactless technologies as occasionally

used sensors. The obtained positioning accuracy mainly depends on the indoor path-loss

awareness and on the markers density, showing that without using markers but dynami-

cally estimating the path-loss exponents we obtain an error of\2 m for 90 % of cases.

Keywords Sensor fusion � Seamless positioning � Smart devices

& Alfonso Bahilloalfonso.bahillo@deusto.es

Teodoro Aguilerateoaguibe@unex.es

Fernando J. Alvarezfafranco@unex.es

Asier Perallosperallos@deusto.es

1 DeustoTech-Fundacion Deusto, Deusto Foundation, Av. Universidades 24, 48007 Bilbao, Spain

2 Electrical Engineering, Electronics and Automation Department, University of Extremadura,06006 Badajoz, Spain

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Wireless Pers CommunDOI 10.1007/s11277-016-3759-x

1 Introduction

Man has always felt the need to position himself using the sun and the stars as the only

reliable reference systems for many centuries. Today, with the advent of global navigation

satellite systems (GNSS) global positioning in the earth’s surface is a successfully over-

come problem. Nonetheless, local positioning in indoor environments is still a matter of

active research, since GNSS signals get severely degraded in this type of environments due

to multipath and attenuation losses, and thus they cannot be used to track people or objects

with acceptable accuracy [1]. Many local positioning systems (LPS) have been developed

during the past two decades based on different technologies that include ultrasound [2–6],

radio-frequency [7–9], vision cameras [10, 11] and magnetic [12, 13], among others. After

all this research effort, it is becoming clear that none of these technologies clearly out-

performs the others, and we expect now a new tendency to design systems that combine

some of them to benefit from their complementary strengths.

In this work we propose a novel seamless positioning system for the fusion of the

sensors information provided by a portable smart device, intended to perform real time

location without interruption and independently of the environment the user is moving,

thus covering the deficiencies of GNSS indoors and in urban outdoor environments with

poor coverage. The capability of smart devices to be used as the mobile node in privacy-

oriented systems, where this node is in charge of computing its own position, and also in

centralized systems, where the node position is computed by a central processor, has been

already demonstrated in several works. Hence, in [14, 15] two privacy-oriented systems

based on the use of Bluetooth beacons are described, where an Audiovox SMT5600 and a

Nokia N70 devices are respectively positioned with an accuracy of 2–3 m. A centralized

system based on the same technology is proposed in [16], achieving accuracies of 2 m in a

12 9 7 m2 laboratory with a Sony Ericsson P800. In [17] a privacy-oriented system is

proposed based on the measurement of the received VHF signals strength to achieve an

accuracy of 1–3 m. A different approach is followed in the centralized system described in

[18], where a PDA HP5550 is positioned by performing the emission of a pure 4.01 kHz

acoustic tone during 100 ms, with accuracies of about 65 cm. Another centralized system

based on the emission of short ultrasound pulses of 21.5 kHz has been recently proposed in

[19] to position an HTC-G1 smartphone with accuracies of about 10 cm in a restricted

environment of 7 9 7 m2. Also, accuracies below 4 m have been reported in [20] by using

the magnetometer of a HTC Nexus One in several corridors of two buildings. All these

works rely on a single technology, though. Some authors have exploited the benefits of

using additional technologies by incorporating external sensor units whose information is

centralized and managed by the smart device. Hence, in [21] a dual IMU system, one

mounted in the torso and a second one mounted in the foot, are used to collect rich

information that is sent to a OpenMoko phone where a simplified map-matching particle

filter estimates the user position. A more complete system is described in [22], where an

external multi-sensor platform that integrates an enhanced GPS receiver and inertial

sensors is used to retrieve the user position when this information cannot be obtained from

the built-in GPS and WLAN receivers of a Nokia N95 smartphone. Unfortunately, the

main disadvantage of these systems is the loss of portability associated with the use of an

external sensor unit. There are also some recent works that propose the fusion of the

information provided only by the different built-in sensors of a smart device. This is, for

example, the case of the system proposed in [23], that implements a Kalman filter to

combine the information obtained from the WiFi engine and the built-in accelerometer and

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digital compass of a Samsung Nexus S, obtaining accuracies between 2.4 and 4.1 m in a

3000 square meters building level. A similar system is proposed in [24], that uses Hidden

Markov Models to combine the information provided by the WiFi engine, the

accelerometer and the compass of a Nokia N8, to achieve accuracies around 3 m in a real

office environment. In both cases, the system has been designed to operate in a calibrated

environment and it is very sensitive to changes in this environment, since the WiFi

positioning engine is based on the comparison on the measured RSS values with those

previously gathered in a fingerprint database.

To the authors knowledge, the seamless positioning system presented in this paper is the

first system to combine a wide diversity of information provided by different sensors of a

single and commercially available smart device with the aim to perform the seamless

positioning of its user both outdoors and in uncalibrated indoor environments, i.e. envi-

ronments whose sensory signals have not had to be measured and analysed beforehand for

later estimation of the user position during real-time operation. The main improvements of

this system with respect to previous directly related works can be summarized as follows:

• The WiFi positioning engine is not based on fingerprinting, what gives the system the

capability to work in uncalibrated environments.

• The information provided by the smart device sensors is combined by means of an

unscented Kalman filter (UKF), a filter that is more accurate than an extended Kalman

filter (EKF) and easier to implement than an EKF or a Gauss second-order filter [25].

• Specifically designed ultrasound markers and the built-in microphone are used to

accurately update the user position in regions of particular interest without the need of

any action from the user side.

This system would be the ideal platform for location based services (LBS), that provide the

user with personalized information depending on his geographic location [26]. The pro-

posed seamless positioning system would easily allow the extension of all those services

that are currently being provided outdoors to indoor environments such as hospitals,

museums, train stations, airports, malls and many others that could undoubtedly benefit

from this type of services. Hence, among the potential applications of WAY are emergency

assistance, indoor routing, car park guidance, personalized advertising, location sensitive

billing and leisure activities such as buddy finder or geocaching. Needless to say, the

involvement of end-users will be necessary to validate the system and to develop these

services. In that sense, the system will ensure confidentiality by not safeguarding sensitive

information, recognizing the participants property over their personal data and their right to

decide on how these data shall be used, share and made public (through informed consent)

and finally, by informing them about how this information will be disposed of.

The rest of the paper is organized as follows. Section 2 presents the general features of

the seamless positioning system, including the management, processing and presentation

blocks. In Sect. 3 a detailed description of the different sensors providing information to

the seamless positioning system is given. Section 4 presents some experimental results

obtained with this system in a real environment composed by a three floor building and its

surroundings. Finally, Sect. 5 outlines the main conclusions that can be drawn from these

results.

WAY: Seamless Positioning Using a Smart Device

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2 Seamless Positioning System WAY

The seamless positioning system proposed in this paper, called WAY (Where Are You?)

performs a seamless localization estimation of the user’s smart device in real time.

Nowadays, the smart devices already integrate a GNSS receiver, a WiFi adapter, a camera

and a microphone, and shortly, most of them will integrate other sensors such as the

barometer and the proximity contactless technology NFC. The aim of WAY is to develop a

framework that estimates the user position in a continuously and transparent fashion by

fusing the so called signals of opportunity (SoOP) which are transmitted for non-local-

ization purposes, but may be exploited to this end. The SoOP chosen for the evaluation of

WAY are the GNSS, WiFi, ultrasound, NFC, the air pressure values and the QR codes.

Thus, WAY fuses information provided by GNSS, NFC tags, QR codes and ultrasound

markers at the position solution level with the information provided by WiFi at the raw

measurements level. In fact, WAY is thought as a modular system for commercially

available smart devices where SoOP coming from new technologies can be easily inte-

grated. To achieve the WAY’s aim, we will take into account criterions such as available

battery, position estimation accuracy, and system continuity and performance. Thus, WAY

will help to develop LBSs which make easier the user-environment interaction.

In Fig. 1 we describe the flowchart that follows WAY to fuse and smartly prioritize the

different SoOP to be used. In general terms, the flowchart that governs WAY is divided

into three blocks: management, processing and presentation. The management block will

decide what signal will get to the processing block. The processing block will fuse the data

ProcessingBlock

Message: Waitingfor Signal

Accurate PosEnvironment Previous

ScanningGNSS

Scanning WiFi

> # Sat > # APsEnable WiFi

Enable GNSS

GettingLat,Lon,Alt

GettingRSS

NO NO

NOYES

YES YES

Tim

e O

ut

Tim

e O

ut

OutdoorIndoor GNSS

WiFi/None

Start

Disable WiFi Disable GNSS

Management Block

Fig. 1 Flowchart of the management block

A. Bahillo et al.

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coming from the management block to infer the user position. Finally, the presentation

block will show the estimated position and it can be used as the interface for LBSs.

2.1 Management Block

This block not only decides what SoOP will get to the processing block but it is also

responsible for what smart device sensor will be enabled. Thus, it plays an important role

in the battery consumption. WAY decides what SoOP will be processed taken into account

the available battery and the environment. Thus, if the user is in an open area, where a line-

of-sight between the smart device and the satellites is guaranteed, the barometer is disabled

and the GNSS receiver will be enabled, thus the three-dimensional geographic coordinates:

latitude, longitude and altitude will be used. However, if the user is inside the perimeter of

a building, the barometer and the WiFi adapter will be enabled, thus the received signal

strength (RSS) coming from the WiFi access points will be used. Although, the man-

agement block keeps using the SoOP based on the environment, near any indoor/outdoor

transition zone, such as doors, both GNSS and WiFi, will be enabled.

As shown in Fig. 1 WAY can start from an accurate position. By accurate position we

mean that, at a certain point in time, WAY has received accurate and reliable information

about the user position. This information could arrive from reading a QR code, an ultra-

sound or NFC tag, or even it can be typed by the user itself. In case WAY starts without

information about the initial position, the WiFi adapter starts scanning for beacon signals

coming from WiFi access points. After a threshold time, if the WiFi signals are not found,

the GNSS receiver starts scanning for satellite signals. Both receivers, GNSS and WiFi, are

enabled searching for signals until one of both fulfils the required condition for getting to

the processing block. In the GNSS case, more than three satellites in line-of-sight. In the

WiFi case, more than two access points in range.

Notice that (1) in case WAY does not know the initial position, it first scans for WiFi

signals because it is faster than GNSS scanning; (2) WiFi scanning does not compromise

the data communication and it does not inject data traffic into the network; and (3) both

receivers, GNSS and WiFi, compromise the battery consumption, a reason why just one

receiver is kept enabled collecting signals1 at a time.

2.2 Processing Block

This block represents the framework which estimates the user’s position fusing the SoOP

gathered by the smart device sensors. Bayesian filters, which use a probabilistic framework

to perform reasoning, are a theoretically sound way to combine multiple and different

SoOP. Bayes filters probabilistically estimate a dynamic system’s state from noisy

observations. They represent the state at time k by random variables xk. At each point in

time, a probability distribution over xk, called belief, represents the uncertainty. Bayes

filters aim to sequentially estimate such beliefs over the state space conditioned on all

information contained in the observations [27]. In this paper, the state is the user’s location,

xk ¼ ½xk; yk; zk�T , while the SoOP provide observations about the state. Among the different

Bayes filters, Kalman filters are the most widely used. They are optimal estimators,

assuming the initial uncertainty is Gaussian, the observation model and system dynamics

are linear functions of the state, and the measurement and process noise distributions are

1 If the battery consumption will not be a constraint, both GNSS and WiFi will succeed to the processingblock.

WAY: Seamless Positioning Using a Smart Device

123

Gaussian. However, the lack of linearity in the models that relates most of the SoOP to the

user’s location implies the usage of a suboptimal solution, where the most common is to

use the EKF. Nevertheless, we selected the unscented Kalman filter (UKF) since it better

captures the higher order moments caused by the non-linear transformation and avoids the

computation of Jacobian and Hessian matrices [25]. Furthermore, the overall number of

computations performed by the UKF are the same order as the EKF, and much lower than

solutions such as the particle filter which better represents the belief but needs a number of

computations unacceptable for most smart devices. By using an UKF as the framework,

WAY finds the trade-offs among accuracy, real time and battery consumption.

Consider the following non-linear system, described by the dynamic and measurement

models with additive noise:

xk ¼ fðxk�1Þ þ wk�1 ð1Þ

zk ¼ hðxkÞ þ vk�1 ð2Þ

where wk and vk are the process and observation noise which are both assumed to be zero

mean multivariate Gaussian noise with covariance Qk and Rk, respectively. The function

f can be used to compute the predicted state from the previous estimate and similarly the

function h can be used to compute the predicted measurement from the predicted state.

On the one hand, the dynamics of the system can be represented as

xk ¼ xk�1 þ _xk�1Dt þ wk�1 ð3Þ

where Dt ¼ ðtk � tk�1Þ is the time step and _xk is the first derivative of the state, in this case,the user’s velocity. Finally, wk is assumed to be a zero-mean Gaussian variable with

covariance matrix Qk. The values of Qk depend on the dynamic of the target, in this paper

a walking person. In practice, Qk is a diagonal matrix whose in-diagonal elements rep-

resent the variance of the user’s position and velocity [28].

On the other hand, the function h depends on the SoOP. In the WiFi case the mea-

surement model, called hw, can be represented as

zwk ¼ a� 10n log 10ðjjxk � APjjÞ þ vwk�1 ð4Þ

where zwk is the RSS measured value; a is a parameter that remains constant in those

scenarios where the antennas gain and the power transmitted from the access points are

also constant, a situation typically found in most WiFi WLANs, (in practice, this value can

be known beforehand from experimental measurements taken in a generic environment

similar to that where the location system is going to operate) [29]; AP is the position of the

WiFi access point; and n is the path-loss exponent corresponding to the actual propagation

environment. In free space n ¼ 2, however in practice, depending on the environment the

path-loss exponents ranging from 1.5 to 4.5 [29, 30]. In this paper we analyze the per-

formance of WAY using the path-loss exponent values under two different situations: (1)

setting fixed values for the path-loss exponents sampling values ranging from 1.5 to 4.5

according to this rule: the stronger the RSS measured value (assuming a closer position to

the antenna) the smaller the path-loss exponent value (assuming lower reflections and

diffractions), and (2) dynamically estimating the path-loss exponent values following the

algorithm proposed in [8]. By using this algorithm we found the path-loss exponents that

best fit the propagation environment between the receiver (user’s smart device) and each

WiFi access point in range at each time interval. vwk represents the RSS noise and it can be

assumed to be zero-mean Gaussian variable with covariance matrix Rwk . In practice, we

A. Bahillo et al.

123

have hwi with i ¼ 1; 2; . . .Mk functions, where Mk is the number of WiFi access points in

range at each time step k. Accordingly, Rwk is a diagonal matrix whose in-diagonal ele-

ments represent the variance of the measurements comming from each WiFi access point.

In the GNSS case, the measurement model, called hg, can be represented as

zgk ¼ xk þ vgk�1 ð5Þ

where zgk is the GNSS position estimation. As the GNSS reports the position in geographic

coordinates, and WAY works in UTM (Universal Transverse Mercator) coordinates which

uses a 2-dimensional cartesian coordinate system to give locations on the surface of the

Earth, we previously perform the corresponding transformation from geographic to UTM

coordinates. vgk represents the GNSS noise which can be assumed to be zero-mean

Gaussian variable with covariance matrix Rgk . In practice, Rg

k is a diagonal matrix whose

in-diagonal elements represent the variance of the measurements comming from the

satellites, and its value depends on the number of satellites in line-of-sight. The more

satellites with good GDOP (Geometric Dilution of Precision), the more reliable the GNSS

data.

In the barometer case, the measurement model, called hb, can be represented as

zbk ¼ b�1ðxk½3�Þ þ vbk�1 ð6Þ

where zbk is the air pressure, xk½3� is the third element of the state, the altitude, and b�1 is

the inverse of the model used to convert the air pressure into altitude. This model, b, will be

explained in the next section. vbk represents the air pressure noise which can be assumed to

be zero-mean Gaussian variable with covariance matrix Rbk .

Finally, in the contactless technologies cases using ultrasound signals, NFC tags and QR

codes, the measurement model, calle hc, can be represented as

zck ¼ xk þ vck�1 ð7Þ

where zck is the position of the ultrasound or NFC tag, or QR code. vck represents the

observation noise which can be assumed to be zero-mean Gaussian variable with covari-

ance matrix Rck. As the SoOP gather by the contactless technologies have to be read at few

centimeters from the tag or code, in practice, Rck is a diagonal matrix whose in-diagonal

elements are lower than 1 meter.

As shown in Fig. 2, and it is well described in [25], once the dynamic and measurement

models, and their noise covariace matrices are described, the UKF is straightforward to

implement. Notice that the initial values of the state covariance matrix, P0, depends on the

initial state confidence. UKF main advantage is their computational efficiency (same as

EKF and lower than particle filters), better linearization than EKF (accurate in the third-

order Taylor series expansion), and derivative-free (no Jacobian and Hessian matrices are

needed) [25].

2.3 Presentation Block

There are many ways of representing the user position and its covariance. On its basic

form, WAY shows an ellipse on a map of the smart device screen, where its centre is the

user position estimate and its major and minor axes represent the uncertainty on each

direction. Furthermore, the user position and its covariance can be sent to a server and

show it remotely, or it can be saved for subsequent evaluation. The format of the position

WAY: Seamless Positioning Using a Smart Device

123

coordinates can be chosen and even the attitude of the user can be inferred from con-

secutive positions. This block will be the interface with LBSs which make easier the user-

environment interaction.

3 Sensors Information

To develop a seamless positioning solution on a smart device we have to find those

technologies which can provide location information, how accurate it could be and where

they could work. In this section we briefly describe the sensors information to be fused,

their characteristics (availability, inputs, outputs, functionality, etc.) and their main role

into WAY. These sensors have been classified into three gruops, namely, the GNSS

receiver, the signals of opportunity, and the ultrasound markers and microphone. It is

important to remark that, although WAY has been designed to interpret and fuse the

information provided by all these sensors, this does not mean that all of them will be

simultaneously active during a regular operation. On the contrary, in most real scenarios

only part of this sensory information will be available, that might be different depending on

the particular features of the environment. The large variety of sensors managed by WAY

confers high versatility to this application, that is always trying to exploit the benefits of

each sensor to achieve a more accurate location in any scenario. These benefits are

described next.

ManagementBlock

MeasurementModel

MeasurementUpdate

TimeUpdate

DynamicModel

PresentationBlock

Lat,Lon,Alt(GNSS)

RSS(WiFi) BarometerContactless

zk, Rk, hk

xk, Pkx0, P0

Qk, Fk Pk|k-1, xk|k-1

Pk|k, xk|k

Processing Block

Fig. 2 Flowchart of the processing block

A. Bahillo et al.

123

3.1 GNSS Receiver

The GNSS receiver which is integrated in most smart devices is extensively used to

estimate the user position in open areas. WAY uses the GNSS signals to infer the user

location where at least three or more satellites were visible. Therefore, WAY beneficts

form the GNSS data only in open areas where it accurately reports the user’s position in

terms of its latitude, longitude and altitude. Usually, GNSS data are already Kalman

filtered inside the GNSS receiver, thus a cascade of two filters take place: internal GNSS

KF and the WAY UKF.

3.2 Signals of Opportunity

As stated in the introduction, WAY uses a new set of SoOP that includes the WiFi adapter

and the barometer as frequently used sensors, and the proximity contactless technologies as

ocassionally used sensors.

3.2.1 Wireless WiFi Adapter

Not only data communication through the wireless ethernet but also position estimation

can be achieved by using the wireless WiFi adapter which is integrated in most smart

devices. By scanning the environment, the smart device can get the RSS of WiFi signals

coming from each WiFi access point in range identified through its physical address. The

benefit of using this SoOP is that WiFi signals predominate inside buildings where GNSS

signals are blocked. Assuming the WiFi access points position is previously known, we can

perform the procedure shown in Sect. 2, which dynamically reports an estimation of the

user position by only using the RSS. WAY frequently uses the WiFi adapter to estimate the

position of the user inside buildings, where the WiFi signals predominate.

3.2.2 The Barometer

Many personal navigation systems will benefit from a barometer because it reports

accurate altitude information inside buildings where GNSS data is blocked. Shortly, it will

be easy to find a barometer integrated in most smart devices which accurately measures the

air pressure. As it is commonly known, pressure measured by barometers can be converted

into altitude information. Altitude can be modelled accurately by the International Stan-

dard Atmosphere (ISA) [31]. Other used models to convert the air pressure into altitude

such as U.S. Standard Atmosphere and WMO Standard Atmosphere are equivalent to the

ISA model up to 32 km over the sea level [32]. The ISA equation for altitude xk½3� is

xk½3� ¼ bðzbkÞ ¼T0

k1� zbk

p0

� �kRairg

0@

1A ð8Þ

where T0 ¼ 288; 15 K is the temperature at sea level, k ¼ �0:0065 K/m is the temperature

gradient, p0 ¼ 1013:25 mbar is the pressure at sea level, Rair ¼ 287 m2(s2 K)-1 is the

atmosphere gas constant, and g ¼ 9:8 m/s2 is the earth gravity. This model is not exact

because it assumes some variables as constants, and it does not take into account other

factors such as humidity, weather, or the presence of air conditioning systems. However,

WAY does not use the absolute but the relative altitude. When available, the absolute

WAY: Seamless Positioning Using a Smart Device

123

altitude is given by the GNSS. The estimated relative altitude is used to infer any building

floor change. Furthermore, it can also be used to distinguish if the user got to a floor

through the lift or the stairs. In the lift case, the observed altitude profile has a constant and

higher slope than in the stairs case. Figure 3 shows the estimated altitude profile corre-

sponding to the path described in Fig. 6 when the target is inside the building. The path

followed has four floor changes: two by using the stairs (going up from ground floor to 1st

floor, and from 1st to 2nd floor), and two by using the lift (going down from 2nd to 1st

floor, and from 1st to ground floor). The observed slope when going up (or down) by lift is

always constant and higher than 0.75 m/s, but when going up (or down) by stairs the slope

is more noisy and it is always lower than 0.5 m/s. Therefore, thanks to the barometer data,

WAY can easily identify when the user is on the stairs or in the lift which also can be used

to update the user position.

3.2.3 Proximity Contactless Technologies

The benefit of using the proximity contactless technologies is that we can set the position

of the user to the position of the marker when reading the marker because the user has to be

placed at few centimeters form the mearker to this purpose. Shortly, these proximity

contactless technologies will be predominant therefore, assuming as previously known the

position of the markers, once on a while WAY will be able to update accurately the

position of the user. Additionally, depending on the context we can configure more actions

on the smart device when the tag is read such as displaying local information, configuring

the wireless connectivities, enabling or disabling sounds, setting calendar issues, etc. [33].

These proximity contactless technologies work anywhere and they can update the location

algorithm with accurate position information.

3.3 Ultrasound Markers and the Microphone

Similarly to the proximity contactless technologies, but without the need of any action

from the user side, the microphone can be used to accurately update the user position in

Fig. 3 Altitude profile of the path followed at the ETSIT (detailed in Fig. 6)

A. Bahillo et al.

123

locations of particular interest. This property derives from the capability of smart devices

microphones to acquire ultrasound for accurate acoustic code identification. The fre-

quencies of the generated emissions are between 18 and 22 kHz, which are high enough to

be inaudible for almost every people but low enough to be generated by standard sound

hardware.

Previous works had shown that it is possible to emit a 21 kHz acoustic signal, just above

the human hearing range, from a smart device speaker and successfully receive it with a

conventional microphone [1, 19, 34]. WAY explores the reversed circumstance, i.e. low

frequency ultrasound signals are generated by specifically designed ultrasound markers

based on the Kingstate KSSG1708 transducer [35], and their reception is carried out by the

smart device, without the need of any additional microphone, acquisition or processing

hardware. To this respect, it is important to note that although the maximum theoretical

value for the frequency response of the built-in device microphone has been specified at

20 kHz by an external analysis [36], we have successfully performed acquisitions at fre-

quencies above 22 kHz. Figure 4 shows the frequency response of the emitter/receiver pair

experimentally obtained in our lab when the microphone is placed at one meter distance

over the acoustic axis of the ultrasound marker.

The ultrasound markers emit 63-bit Kasami sequences BPSK modulated with a symbol

of four cycles of a 20 kHz carrier. The good autocorrelation and crosscorrelation properties

of these codes allows the identification of different emissions in very noisy environments.

However, to avoid hearing these emissions, some additional processing is necessary. All

codes are band-pass filtered with lower and upper passband frequencies of 18 and 22 kHz

respectively, and then Hamming-windowed to attenuate the transient effects. Figure 5

shows the temporal and spectral features of Code 1 before (a) and after (b) this processing.

The acquisition of audio signals with an Android device requires the implementation

and configuration of the class which manages the audio resources for Java applications.

The system sampling rate was set to 48 kHz and the buffer capacity was chosen accord-

ingly to assure the acquisition of at least one complete code. Once acquired, the samples

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23−40

−35

−30

−25

−20

−15

−10

−5

0

Frequency (kHz)

Nor

mal

ized

Sou

nd P

ress

ure

Leve

l (dB

)

Fig. 4 Experimental frequency response of the ultrasound marker/smart device microphone pair

WAY: Seamless Positioning Using a Smart Device

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are processed making use of a library of basic DSP algorithms that allows the identification

of the emitted signals by matched filtering and thresholding.

The microphone works anywhere, but WAY only uses it when the estimated position is

in the proximity of a ultrasound marker, thus alleviating the smart device processor

computational load and reducing the power consumption.

4 Experimental Results

WAY has been developed on Android, a mobile operating platform which nowadays

controls most of smart devices. As a way to verify its behaviour under a real environment it

has been tested using the GNSS, WiFi and barometer as frequently used sensors, and the

microphone and the proximity contactless technologies as occasionally used sensors.

4.1 Experimental Setup

For the experimental evaluation we conducted real trials at the building of the Higher

Technical School of Telecommunications (ETSIT) in the city of Valladolid, whose floor

map is shown in Fig. 6. This three floor building is 125 m long and 75 m wide. In the

experiments the target is a person who carries the Galaxy Nexus GT-i9250 smartphone as

smart device. This smartphone incorporates the broadcom BCM4330 single chip device

providing with integrated IEEE 802.11 a/b/g among other handheld wireless systems, the

Bosch BMP180 digital barometric pressure sensor as barometer, the CSR GSD4t as

internal satellite signal tracking engine, the GH59-11319A module as microphone, and the

NXP PN533 NFC controller.

0 200 400 600

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Fig. 5 Temporal and spectral features of Code 1 before (a) and after (b) processing

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As WiFi access points we used the Trapezee MP372 already deployed at the ETSIT

building which send a beacon frame each 100 ms at constant power on frequency channels

1, 6 and 11 (around 2.412, 2.437 and 2.462 GHz, respectively). The building contains 8

WiFi access points at the ground floor, 10 at the first floor, and 11 at the second floor. Thus,

we have approximately one WiFi access point each 900 m2. As QR codes we printed three

matrix barcodes which encode an URL direction pointing to a XML file. The XML file

contains information about the actual position of the QR code among other contextual

information. As NFC tag we used the NXP MF1 S70 smart card with a writable 4096 bytes

EEPROM. Equally, the NFC tag contains an URL direction which points to a XML file

that contains contextual information and its actual position. The ultrasound markers used in

the experimental setup have been specifically designed for this work. They are based on the

Kingstate KSSG1708 low power transducer, the LM386 low power audio amplifier and the

LPC1768 microcontroller, which stores the 2520 samples of the BPSK modulated Kasami

code and dispatch them at a 200 kS/s rate every 20 ms.

The path shown in Fig. 6 tries to cover all the situations that a user can face up when

walking inside a building with an uncalibrated WiFi network. The path contains outdoor to

indoor and indoor to outdoor transitions, floor changes by stairs and lift, and wide and

narrow corridors. The user was following the path walking through the corridors and

offices of the three floors where at all times there were people walking around. In this case

the initial position was established by the GNSS measurements because the path starts in

an open area. Figure 6a–c show the actual path in red slash, the estimated positions in blue

diamonds, and the position of the WiFi access points, QR codes, NFC and ultrasound

markers deployed in the ETSIT ground, first, and second floors.

Fig. 6 Path followed carrying the smartphone at the ETSIT building. In red slash the actual path, in bluediamonds the estimated positions. a Second floor, b first floor and c ground floor. (Color figure online)

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4.2 Results

In Fig. 6 we observe that at first the user is outside in an open area where the GNSS signals

are of good quality. Thus, when outside WAY is only using the GNSS observations, i.e. hg

and Rgk . Prior to enter the building we have a QR code with information about the building

services. When reading the QR code WAY incorporates the accurate position information

hidden in the QR code, i.e. uses hc and Rck values. Once the user is inside the building, the

GNSS signals are weaker enough to enable the WiFi adapter. In few seconds the GNSS is

disabled and the WiFi signals and pressure observations start to be used, i.e. hwi and Rwk;i.

The WiFi signals are strong enough to be used during the time the user is inside the

building. Thus, while inside the building, the observed positions are performed by the

WAY algorithm with the WiFi RSS observations. Once in a while, an accurate position

coming from the barometer, the QR codes, NFC or ultrasound markers update the WAY

algorithm, i.e. uses hc and Rck values. The markers density is sufficiently low to avoid

masking the accuracy of the whole system.

The barometer sensor controls the relative altitude profile. When it detects a change

higher than 1.5 m the user position is updated to the nearest stairs or lift location (we

assume the stairs and lift locations to be previously known). After a few seconds the slope

of the altitude profile differentiates between transitions by stairs or lift. Finally, when the

altitude profile detects a change equal or higher than the floor height the planimetry is

automatically updated to the current floor on the smart device screen.

Other source of valuable information comes from the markers. When reading QR codes

or NFC tags the WAY scheme is updated with the corresponding accurate position of the

marker. Similarly to these proximity contactless technologies, but without the need of the

user action, works the microphone sensor. This property derives from the capability of

smart devices’ microphones to acquire ultrasound for accurate acoustic code identification.

Each ultrasound marker emits an ultrasound code which identifies it unambiguously. Equal

to the barometer sensor, the advantage of ultrasound technology is that the smart device

automatically detects the measurements without the need of any user action.

For the evaluation of the error distribution, we compared the obtained positions with a

ground truth measured using marks in the walking path. The cumulative distribution

function (CDF) of the positioning errors is presented in Fig. 7. The proposed WAY is able

to obtain positions errors of \8 m for 90 % of the samples when using fix path-loss

exponents and a few markers such as the QR codes, NFC tags or ultrasound beacons which

once in a while update the estimated position. This error increases up to 12 m for 90 % of

samples without using the markers. We compared these two CDFs to the one obtained by

dynamically estimate the path-loss exponents that best fits the propagation environment in

real time without the need of any calibration stage, but only using the actual RSS mea-

surements, following the algorithm proposed by Mazuelas et al. [8], in both cases with and

without markers. When the path-loss exponents are dynamically estimated the positions

errors are lower than 2 m for 90 % of samples. These results show the importance of the

path-loss exponents values when working with RSS measurements.

Finally, with the aim of analyzing the WAY battery consumption, in Fig. 8 we show the

battery charge comparison of three Samsung smartphones, Note 2, Nexus 3 and Nexus 5

when running the WAY application. After 40 minutes, we observe that the smartphones

Note 2, Nexus 3 and Nexus 5 reduce the battery charge 3, 6 and 4 %, respectively, when

the WAY application is running with respect to the situation where only a white screen is

shown. The differences observed among the different smartphones are mainly because

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their batteries have been used for different periods of time along their lives. However, we

have observed that on average, the WAY application uses around 87mAh of the battery

when it is running with all the sensors enabled.

5 Conclusions

In this paper we have presented an UKF scheme for the real time fusion of the SoOP

provided by a portable smart device sensors to guarantee a seamless positioning system.

The flowchart that follows WAY smartly combines and prioritizes the different SoOP to be

fused. The GNSS signal provides a good three-dimensional position estimation in open

areas, while the WiFi RSS provides a good two-dimensional estimation in indoor envi-

ronments. The barometer provides a good estimation of the relative altitude profile indoors,

thus, this sensor extends the indoor positioning to 2.5 dimensions. Additionally, the

markers such as the QR codes, NFC and ultrasound can be used to accurately, and once in a

while, update the estimated position. The QR and NFC markers need to enable the camera

or the NFC technology, respectively, while the ultrasound markers do not need any action

from the user side. WAY is thought as a modular system, therefore, new SoOP which can

be related to the user position can be easily integrated, such as map information, user

acceleration, rate of turn or magnetic north. In fact, the next step is to integrate the inertial

sensors of the smart device in the UKF to better implement the dynamic model.

The proposed seamless positioning system called WAY has been tested with a com-

mercially available smart device in an uncalibrated real environment. It performs real time

location without interruption and independently of the environment. WAY is able to obtain

positions errors of \8 m for 90 % of the samples when using a few markers which

occasionally update the estimated position. This error increases up to 12 m for 90 % of

samples without using the markers. However, when the path-loss exponents are dynami-

cally estimated, in both cases with and without markers, the positions errors improve taken

values lower than 2 m for 90 % of samples. Therefore, indoors the obtained positioning

0 5 10 15 20 25 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

[m]

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age

WAY using known tags and dynamically estimating the path−loss exponentsWAY dynamically estimating the path−loss exponentsWAY using fixed path−loss exponents and tagsWAY using fixed path−loss exponents

Fig. 7 Cumulative distribution function of the position error for WAY using known and fixed path-lossexponents with and without markers

WAY: Seamless Positioning Using a Smart Device

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accuracy mainly depends on the indoor path-loss awareness. Even the markers could be

dispensable if we use the path-loss exponents that best fit the propagation path between the

target and each WiFi access point in range.

Acknowledgments This work has been supported by the Spanish Ministry of Economy and Competi-tiveness under the ESPHIA project (ref. TIN2014-56042-JIN) and TARSIUS project (ref. TIN2015-71564-

0 5 10 15 20 25 30 35 4090

92

94

96

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100

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tery

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perc

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White ScreenWAY

0 5 10 15 20 25 30 35 4085

90

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White ScreenWAY

0 5 10 15 20 25 30 35 4092

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White ScreenWAY

(a)

(b)

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Fig. 8 Battery charge percentage comparison of Samsung. a Note 2, b Nexus 3 and c Nexus 5

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C4-4-R). The authors would like to thank Dr. Jose M. Villadangos for helping with the hardware design ofthe ultrasound marker.

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Alfonso Bahillo got his Telecommunications Engineering and PhDdegrees at the University of Valladolid, Spain, in 2006 and 2010,respectively. He got the PMP certification at the PMI in 2014. From2006to 2010 he joined CEDETEL working as research engineering. From2006 to 2011 he has beenworking as assistant professor at the Universityof Valladolid. From 2010 to 2012 he joined LUCE Innovative Tech-nologies as product owner. Currently he is working as postdoc at theUniversity of Deusto and project manager at DeustoTech in Bilbaowhere he trains PhD students and collaborates in several national andinternational research projects. He has worked (leading some of them) inmore than 20 regional, national and international research projects andcontracts. He is co-author of 17 research manuscripts published ininternational journals, more than 30 communications in internationalconferences, and 3 national patents.His interests include local and globalpositioning techniques, ambient assisted living, intelligent transportsystems, wireless networking, and smart cities.

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Teodoro Aguilera obtained his Physics degree from the University ofExtremadura (Spain) in 2009. He is currently an Assistant Professor ofAutomation in the Department of Electrical Engineering Electronicsand Automation at the University of Extremadura, Spain. He is also amember of the Sensory Systems Group of this University, where he ispursuing his Ph.D. degree on the topic of ultrasonic local positioningsystems

Fernando J. Alvarez received his Physics degree from the Universityof Sevilla (Spain), his Electronics Engineering degree from theUniversity of Extremadura (Spain) and his Ph.D. degree in Electronicsfrom the University of Alcala (Spain) in 2006. He is currently anAssociate Professor of Digital Electronics in the Department of Elec-trical Engineering Electronics and Automation at the University ofExtremadura, Spain, where he is also the head of the Sensory SystemsGroup. His research areas of interest include local positioning systems,ultrasonic signal processing and embedded computing

Asier Perallos hold a Ph.D., M.Sc. and B.Sc. in Computer Engineeringfrom the University of Deusto. Since 1999 he has been working as alecturer in the Faculty of Engineering at the University of Deusto. Histeaching focuses on software design and distributed systems, havingtaught several B.Sc., M.Sc. and Ph.D. courses. He is currently the Headof the Computer Engineering Department and Director of M.Sc. inSoftware Engineering at the University of Deusto. He is also HeadResearcher at the DeustoTech Mobility Unit at the Deusto Institute ofTechnology, where he coordinates the research activities of around 25researchers. This research unit promotes the application of ICT toaddress smarter transport and mobility. In particular, Perallos’ researchbackground is focused on telernatic systems, vehicular communicationmiddleware and intelligent transportation systems

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