1. No category

How to Solve GNSS Problem in Critical
Environment?
P. Brida*, M. Mlynka* and J. Machaj*
*
University of Zilina/FEE - Department of Telecommunications and Multimedia, Zilina, Slovakia
[email protected], [email protected], [email protected]
Abstract—Information about user position is very important
due to increasing interest in Location Based Services
(LBSs). Nowadays, service providers try to provide
ubiquitous LBSs. It leads to solve ubiquitous positioning, i.e.
localize mobile user anywhere and anytime. This challenge
mainly depends on the actual environment where user
position is determined. The providers try to divide all
environments to two basic types: outdoor and indoor.
Positioning in both types requires different ways to estimate
position. This paper is focused on outdoor environment.
GNSSs (Global Navigation Satellite Systems) provide
acceptable reliability in outdoor, but are not very reliable in
dense urban areas. Entrances to buildings can also be
considered as critical environment,
because
of
interconnection between outdoor and indoor. Therefore
user positioning should be as reliable as possible. Generally,
tall buildings cause the biggest problems, because mobile
device is in GNSS signal shadow and positioning result is
negative affected. This paper analyses these problems and
try to propose a solution. The proposal is based on
alternative positioning solutions based on WLAN, GSM
network and Android Location Provider (ALP). These
systems are compared to GPS (Global Positioning System)
from accuracy point of view.
I.
INTRODUCTION
Location based services (LBSs) attract more and more
users every year. The amount of LBSs is expected to
significantly grow over next years. Therefore positioning
technologies that provide means to localize mobile
devices in unknown environments are interesting for
research in order to provide more reliable, more accurate
and generally better results for users.
Modern LBSs are offered not only in outdoor, but also
in indoor environment [1]. Many of these services are
useful for daily life from the social point of view, e.g.
passenger navigation at airports, vehicle navigation or
patient monitoring in hospitals [2], [3]. Last mentioned
group is very important, because patient is monitored
mainly from health point of view. In case of the patient
health problems, physician knows his position. The patient
can move in both indoor and outdoor environments.
Therefore the patient position needs to be determined
everywhere.
Positioning in both types of environment requires
different ways to accurately estimate position. Indoor
positioning is mainly based on WLAN (Wireless Local
Area Network) – Wi-Fi infrastructure utilization and
various methods use sensors mounted on the user.
Outdoor positioning seems to be solved by GNSSs
(Global Navigation Satellite Systems). These ensure
acceptable reliability in outdoor. On the other hand, we
have to say that GNSSs are not so ideal. They are not very
reliable in dense urban areas where direct view on
satellites is often not presented. The most critical
environment for these LBSs is entrances to buildings.
Entrance to a building is interconnection between outdoor
and indoor therefore user positioning should be as reliable
as possible. GNSSs are not so reliable near buildings,
because user (mobile device) is in the GNSS signal
shadow and positioning results are negatively affected.
Negative impacts are longer time to first fix if position is
determined, positioning accuracy falls down etc. Finally,
we can say that reliability is not sufficient in the specific
areas for this kind of services. Therefore we analyze this
problem and try to find a solution. There are more real
possibilities how to replace GNSS with alternative
positioning system which will be able to estimate position
instead of GNSS in the specific areas. These alternative
systems should be more reliable with requested
positioning accuracy. We will test positioning systems
based on Wi-Fi and GSM network described in [4], [5].
These results will be compared with currently widely used
solution implemented in all Android smartphones. We
will call it Android Location Provider (ALP) in this paper.
This solution determines position based on availability of
cell tower and Wi-Fi access points [6]. Performance of
GNSS will be compared with three different positioning
solutions in real live experiments. The only currently
operational GNSS is Global Positioning System (GPS). It
will be compared to the other positioning possibilities in
terms of accuracy and reliability.
The rest of the paper is structured as follows. Section 2
introduces relevant positioning systems. In Section 3,
experimental setup is presented. In Section 4, the
experimental results are presented and discussed.
Section 5 concludes the paper and suggests some future
studies.
II. TESTED POSITIONING SYSTEMS
As noted above GPS and three alternative positioning
systems will be tested in this paper. Principle of GPS will
be roughly explained. Detailed explanation can be found
in [7]. Two tested positioning systems based on Wi-Fi and
GSM were designed at the University of Zilina. System
based on Wi-Fi is called WifiLOC [5]. System based on
GSM network was designed in [4]. Basic principles of
WifiLOC are described in the following part.
C. Fingerprinting
The fingerprinting method relies on a uniqueness of
radio fingerprints in a similar way than forensic science
does with human fingerprints. The radio fingerprints are
vectors of miscellaneous radio signal parameters such as
received signal strength, timing advance or angles. These
vectors are coupled with position coordinates and
altogether form a database of well-known spots –
reference points, where these parameters are known.
Fingerprinting has two phases, so-called offline and online
phase, which are described in following sections.
Figure 1. The architecture of the WifiLOC system
A. WifiLOC Positioning System
WifiLOC is primarily used in indoor, but it can also be
implemented in the outdoor environment. WifiLOC
utilizes signal information for positioning from
surrounding Wi-Fi networks. The system is based on the
fingerprinting positioning method and signal strength
information.
WifiLOC is implemented as a mobile-assisted
positioning concept. It means that the necessary
measurements are done in a localized mobile station and
measured results are forwarded to the network part
Localization Server (LCS). The position is estimated
(calculated) on a server side. The architecture of the
system is depicted in Fig. 1.
The system is based on the client-server architecture.
The entire architecture could be divided into the three
almost independent parts:
• localization server,
• network of reference stations (access points),
• mobile station - user, client.
B. GSM Based Positioning System
Principle and architecture of the GSM based
positioning systems is very similar to WifiLOC. In this
case, Base Stations (BSs) forms network of reference
stations.
Both systems utilize fingerprinting positioning method.
It is described in the following section.
Offline phase
Area where localization services will be offered is
divided into small cells. Each cell is represented by one
reference point (see Fig. 1). Reference points are
represented by geographic coordinates. Information about
Received Signal Strength (RSS) values from all reference
stations (AP or BS) in range are measured at each
reference point. Element of radio map has the form:
Pj = ( N j , α ji , β ji ,θ j ), j = 1,2..., m, (1)
where Nj is number of j-th reference point, m is the
number of all reference points, αji is the vector of RSS
values,βji stands for the identifier of APs and parameter θj
obtains additional information which can be used during
the localization phase.
Values βji are tagged by Media Access Control (MAC)
address and Cell identity (CID) for Wi-Fi and GSM
networks, respectively [8]-[11].
Online phase
During the online phase the server uses a deterministic
nearest neighbor algorithm to estimate the location of the
mobile device. Actual measured RSS values received by
the Smartphone are compared with the values Pj stored in
the database using the Euclidean distance. Euclidean
distance represents the shortest distance between two
vectors in Cartesian coordinate system and is defined by:
n
d Eij = (∑ aik − b jk ) 2 (2)
k =1
where n is number of elements in vector, aik represents kth element of vector A and bjk represents k-th element of
vector B. Position of the reference point with the smallest
Euclidean distance is considered as the estimated position
[8]-[11].
Figure 2. Radio map for fingerprinting using RSS
D. Android Location Provider
Android SDK includes localization library which offers
mobile device localization by a network provider function.
We called it Android Location Provider (ALP) in this
paper. This function determines the location of the mobile
device based on availability of cell towers and Wi-Fi APs.
Results are retrieved by mean values of a network lookup.
This module does not provide high accuracy. On the other
hand, this module can provide localization in an unknown
urban environment [12], [13].
TABLE I.
AVERAGE LOCALIZATION ERROR OF THE PARTICULAR POSITIONING
SYSTEMS
Positioning systems
Localization error [m]
Figure 3. Experimental area – University of Zilina campus
E. Global Positioning System
GPS system was made available to civilians in 1996 for
navigation purposes, it is free of charge. An unobstructed
line of sight from the receiver to the satellites is necessary
to obtain a location. The accuracy of the position estimate
depends on the number of used satellites and satellite
geometry. It is clear that GPS is not able to localize
mobile device in critical areas.
The achieved localization error by standard GPS
chipset implemented in smartphones can be in the range of
4 m in the open outdoor environment. In the urban
environment the accuracy can significantly decrease.
III. EXPERIMENTAL SETUP
Generally, GPS works reliable in outdoor environment.
As was mentioned above, critical areas are places near to
buildings because GPS is not able to quickly and precisely
fix position. Therefore we decided to analyze this area
from noted parameters point of view. As shown in the
Fig. 3, area near the buildings was chosen. In this area
poor GPS coverage was expected.
GPS
26.31
WifiLOC
4.82
GSM
5.32
ALP
69.89
Investigated area is 22x16 meters large. Measurements
during the offline phase were performed in a grid, with
points spaced 2 m apart. Existing radio infrastructure with
three added AP was used. 18 APs and 11 BTSs were
detected in total. Measurements were performed using
HTC Legend smartphone. This smartphone is equipped
with all necessary platforms: GPS, GSM, Wi-Fi and it is
able to localize by means of Android Location Provider.
Firstly, geo-points in a chosen area were selected.
These geo-points were targeted by Trimble VX. Chosen
geodetic method guarantees targeting points with
localization error of 4 cm. These points are accurate
reference points and will be used as reference for four
tested positioning systems.
Radio maps for positioning systems based on
fingerprinting positioning methods need to be created.
Process of radio map creation is depicted in Fig. 4.
During the offline phase, fingerprints by GSM and
Wi-Fi positioning system were created in all targeted
points. These fingerprints were sent to the localization
server and stored in the radio map database. All
preliminary steps are done and evaluation of four
positioning system can be performed.
100 random position estimates on the observed area
were performed to evaluate the performance of individual
localization system. It means that we achieved 100 results
for each system, i.e. 400 results.
Localization error of particular system was calculated
as distance between real - precise position (obtained by
Trimble VX) and the estimated position. This distance
was obtained by Vincenty formula [14]. Vincenty formula
is commonly used in the geodesy to calculate the distance
between two geo-points in WGS 84 system. Obtained
results were statistically processed and are analyzed in the
next section.
IV. EXPERIMENTAL RESULTS
This section analyses experimental results. Mean values
of positioning error of the individual positioning systems
are shown in Table 1. In Fig. 5-8, CDF (Cumulative
Distribution Function) of positioning error for the
individual positioning systems are shown.
Empirical CDF
1
0,9
0,8
0,7
CDF
0,6
0,5
0,4
0,3
min: 1.3051
max: 50.4378
mean: 26.3132
median: 29.6795
0,2
0,2
0
Figure 4. Process of radio maps creating
0
5
10
15
20
25
30
35
Localization error [m]
40
45
50
55
Figure 5. Location error CDF of GPS module measured by
smartphone HTC Legend
60
Empirical CDF
1
0.9
0.8
0.7
CDF
0.6
0.5
0.4
0.3
min: 0
max: 10.7083
mean: 4.8169
median: 4.0831
0.2
0.1
0
0
1
2
3
4
5
6
7
Localization error [m]
8
9
10
11
12
Figure 6. Location error CDF of WifiLOC
Empirical CDF
1
0.9
0.8
0.7
CDF
0.6
0.5
0.4
0.3
min: 0
max: 12.8304
mean: 5.3226
median: 4.4079
0.2
0.1
0
0
1
2
3
4
5
6
7
8
9
Localization error [m]
10
11
12
13
14
Figure 7. Location error CDF of GSM positioning system
Empirical CDF
1
0.9
0.8
0.7
CDF
0.6
0.5
0.4
0.3
min: 11.2308
max: 234.0618
mean: 69.8860
median: 19.0924
0.2
0.1
0
0
25
50
75
100
125
150
Localization error [m]
175
200
225
250
Figure 8. Location error CDF of Android Location Provider
As shown in Fig. 5, GPS localization error in observed
critical area is poor. Median value of the positioning error
is app. 30 m and it is not acceptable for LBSs as
navigation when mobile device should be navigated to the
entrance of building. Maximum error was 50.43 m. Poor
performance of the GPS in this environment was assumed,
therefore we tested alternative solutions.
Promising results were achieved by both WifiLOC and
GSM based positioning systems. Results are depicted in
Fig. 6 and Fig. 7. WifiLOC achieved slightly better
results; median values are app. 4 m and 4.4 m
respectively. Maximum achieved errors were 10.71 m and
12.83 m respectively. In these cases, positioning error is
approximately 87 % smaller compared to GPS.
Positioning results obtained by means of Android
Location Provider are shown in Fig. 8.
Android Location Provider positioning results are better
than GPS, but worse when compared to the best solution
WifiLOC. Median value is 19.1 m and maximum error
was 234 m. These results seem to be not sufficient for
navigation. Exact principle of this positioning solution is
not generally known. Therefore we are not able to
determine why the poor positioning accuracy was
achieved.
Optimistic results of Wi-Fi and GSM based solutions
were caused by a good necessary infrastructure and a high
quality of the radio map. The radio map creation is the
main disadvantage of these systems. On the other hand,
ALP does not need radio map creation and it is more
flexible solution.
On the basis of the achieved results we recommend to
implement alternative positioning systems in the areas
where problems with GPS are assumed. Obviously, some
necessary additional steps should be performed, but
provided positioning estimates are more reliable.
V. CONCLUSION AND FUTURE WORK
Main goal of the paper was to determine problems of
GNSSs in critical areas. We assume that areas near
buildings are critical and crucial to provide reliable LBSs.
In light of these assumptions we defined experimental
scenario and performed extensive real live measurements.
We analyzed four different positioning solutions: standard
GPS, WifiLOC, GSM based solution and Android
location provider implemented in all smartphones
equipped with Android operation system.
The achieved results confirmed our assumption that
GNSS (e.g. GPS) is not always the best solution in the
outdoor environment. Reliability and accuracy of the
system was decreased under the acceptable level. On the
basis of achieved results, it can be concluded that
WifiLOC and GSM based solution offer better accuracy
near the buildings compared to GPS and ALP. These
systems seem to be reliable alternative solutions against
the GPS in the mentioned critical areas.
It is very important from seamless positioning point of
view. These systems can be built on surrounded
infrastructure. The radio map creation is the only
disadvantage of these systems. But radio map creation can
be implemented by simulation tools without big effort.
Combination of all tested solutions can lead to reliable
and ubiquitous positioning system. This positioning
system can ensure providing of modern LBSs in the both
outdoor and indoor environments simultaneously.
ACKNOWLEDGMENT
This work has been partially supported by the Slovak
VEGA grant agency, Project No. 1/0394/13 and by
REFERENCES
[1]
[2]
Ondrus, J., Dicova, J. “Navigation system Galileo – support
technology for ITS,” Journal of Information, Control and
Management Systems, vol. 9, no. 3, pp. 435-442, 2011.
O. Krejcar, L. Motalova, “Home care web services evaluation by
stress testing,” 1st International Conference on e-Technologies
and Networks for Development (ICeND 2011), Dar-es-Salaam,
Tanzania, Springer, E-technologies and networks for development,
Communications in Computer and Information Science, vol. 171,
pp. 238-248, 2011.
[3]
[4]
[5]
[6]
[7]
[8]
D. Vybiral, M. Augustynek, M. Penhaker, “Devices for position
detection,” Journal of vibroengineering, vol. 13, iss. 3, pp. 531535, 2011.
J. Benikovsky, P. Brida, “Positioning system based on
fingerprinting method for mobile cellular networks,” The
Mediterranean Journal of Computers and Networks, vol. 5, pp.
59-67, iss. 2/2009.
P. Brida, F. Gaborik, J. Duha and J. Machaj, “Indoor positioning
system designed for user adaptive systems,” in New Challenges
for Intelligent Information and Database Systems, ser. Studies in
Computational Intelligence, vol. 351, ch. 24, pp. 237-245, April
2011.
http://developer.android.com/reference/android/location/Location
Manager.html#NETWORK_PROVIDER, cited 2013-03-01.
E.D. Kaplan, Understanding GPS, principles and applications,
Artech House, London, UK, 1996.
P. Bahl, V.N. Padmanabhan, “RADAR: An In–building RF–based
user location and tracking system,” in Proceedings of the IEEE
Infocom, 2000.
[9]
[10]
[11]
[12]
[13]
[14]
P. Krishnan, A.S. Krishnakumar, J. Wen-Hua, C. Mallows, “A
system for LEASE: location estimation assisted by stationary
emitters for indoor RF wireless networks,” INFOCOM 2004: 23rd
AnnualJoint Conference of the IEEE Computer and
Communications Societies Gamt, 2004.
A. Cizmar, J. Papaj, L. Dobos, “Security and QoS integration
model for MANETS,” in Computing and Informatics, vol. 31,
iss. 5, pp. 1025-1044, 2012, ISSN 1335-9150.
S. Reddy, A. Bagaria, D. Aggarwal, N. Arora, “Analysis of
fingerprint data in cellular networks: An Android application case
study,” IPCSIT 2012, vol. 47, 2012.
GOOGLE INC., Android Developers, [online] 2012,
http://developer.android.com/.
L. Darcey, S. Conder, Android Wireless Application Development,
3rd ed., Addison-Wesley Professional, 2012.
Vincenty formula for distance between two Latitude/Longitude
points,
[online]:
2013,
http://www.movabletype.co.uk/scripts/latlong-vincenty.html