1. i
A SURVEY AND ANALYSIS OF STATE-OF-THE-ART INDOOR
POSITIONING SYSTEMS
by
Austin Glassner
An Engineering Project Submitted to the Graduate
Faculty of Rensselaer Polytechnic Institute
in Partial Fulfillment of the
Requirements for the degree of
MASTER OF ENGINEERING
Major Subject: Mechanical Engineering
Glassa3@rpi.edu
Approved:
_________________________________________
Sudhangshu Bose, Project Adviser
Rensselaer Polytechnic Institute
Hartford, Connecticut
July 31, 2014
3. iii
ACKNOWLEDGMENT
I want to thank you Mom and Dad for always believing in me and raising me to become
who I am today, Cole and Shanna for being my best friends and pushing me to be better,
Grandma Oranges for knocking some sense into me when I needed it, and Ashley for let-
ting me exchange Ashley time for Project time without ever feeling guilty about it.
Also, thank you Professor Sudahangshu Bose for the time and interest you took in my
project as my advisor.
4. iv
CONTENTS
A SURVEY AND ANALYSIS OF STATE-OF-THE-ART INDOOR POSITIONING
SYSTEMS .................................................................................................................... i
ACKNOWLEDGMENT ..................................................................................................iii
LIST OF TABLES............................................................................................................ vi
LIST OF FIGURES .........................................................................................................vii
TABLE OF SYMBOLS .................................................................................................viii
KEYWORDS.................................................................................................................... ix
ABSTRACT ...................................................................................................................... x
1. INTRODUCTION/BACKGROUND.......................................................................... 1
1.1 Signal Measurement Methods............................................................................ 2
1.1.1 Time Based Methods.............................................................................. 2
1.1.2 Angle Based Methods ............................................................................ 4
1.1.3 Received Signal Strength Methods........................................................ 4
1.2 Location Estimation Techniques........................................................................ 5
1.2.1 Triangulation.......................................................................................... 5
1.2.2 Angulation.............................................................................................. 6
1.2.3 Scene Analysis ....................................................................................... 7
1.2.4 Proximity.............................................................................................. 13
1.3 Mediums Used ................................................................................................. 13
1.3.1 Radio Waves ........................................................................................ 14
1.3.2 Infrared................................................................................................. 15
1.3.3 Ultrasound............................................................................................ 16
1.3.4 Electromagnetic Waves........................................................................ 16
1.3.5 Sensor Networks .................................................................................. 16
1.3.6 Audible Sound...................................................................................... 17
1.3.7 Visual ................................................................................................... 17
5. v
2. METHODOLOGY/APPROACH.............................................................................. 18
3. RESULTS AND DISCUSSION................................................................................ 21
3.1 Results.............................................................................................................. 21
3.2 Discussion ........................................................................................................ 22
3.2.1 Infrared Results.................................................................................... 22
3.2.2 Radio Frequency Based Systems ......................................................... 23
3.2.3 Results for Systems Using Multiple Mediums..................................... 23
3.2.4 Other Medium Results ......................................................................... 23
3.2.5 Other Results........................................................................................ 24
4. CONCLUSIONS ....................................................................................................... 25
REFERENCES ................................................................................................................ 26
APPENDIX A: TECHNOLOGY SPECIFIC DATA...................................................... 29
6. vi
LIST OF TABLES
Table 1: Survey Results................................................................................................... 21
Table 2: Survey Results Continued ................................................................................. 22
7. vii
LIST OF FIGURES
Figure 1: Location hyperbola created by two receivers..................................................... 3
Figure 2: Location hyperbolas created by three receivers................................................. 3
Figure 3: Triangulation Example [13] ............................................................................... 5
Figure 4: Angulation Example [42]................................................................................... 7
Figure 5: 1D RSS Fingerprint............................................................................................ 8
Figure 6: 2D RSS Fingerprint............................................................................................ 9
Figure 7 Precision vs. Number of Access Points [16]..................................................... 11
Figure 8: Precision vs. Path Loss Exponent [16]............................................................. 12
Figure 9: Proximity Example [13]................................................................................... 13
8. viii
TABLE OF SYMBOLS
Symbol Description Units
𝑅 Distance from transmitter to receiver in
an IPS system
m
t Travel time for a signal to go from trans-
mitter to receiver
s
c Speed of the signal m/s
Z Euclidean distance m
p Actual RSS measurement dBm
r Stored RSS measurement dBm
10. x
ABSTRACT
Recently, wireless indoor positioning systems (IPSs) have become a popular way to pro-
vide location information for persons and devices. This location information has been
successfully used in hospitals, warehouses, and malls. The present project delivers a high
level synopsis of the signal measurement methods, location estimation techniques, and
mediums currently used for IPSs along with a comprehensive survey of existing IPS so-
lutions that are both commercial and research oriented. Evaluation criteria of each system
are proposed and include cost, performance, robustness, complexity, scalability. The fo-
cus of this project is to compare existing IPSs and offer insight into which environment
each system would be best used.
11. 1
1. INTRODUCTION/BACKGROUND
The proliferation of mobile devices has given many technologies an important role in peo-
ple’s day-to-day lives. One technology in general allows for a user’s mobile device to
become part of a positioning system. The mobile device can use this position and make
the device available for positioning-based services such as navigating. In the past, Global
Positioning Systems (GPS) were used to provide these services. However, GPS cannot
be deployed indoors because they require line of sight transmissions between receivers
and satellites. This is because the signals they emit are too weak to penetrate buildings.
Therefore a new localization service needs to be used to operate in an indoor environment.
Creating a system to operate indoors is not as simple as increasing the strength of GPS
signals. An outdoor environment is very different from an indoor environment. While an
outdoor environment is large and full of open space, an indoor environment can be small
and full of interference, making an indoor environment more complex. This interference
can come in the form of multipath effects and noise. Multipath effects are caused when
outside sources influence the propagation of electromagnetic waves. They can be caused
by walls, equipment, and humans themselves. Noise often comes from other wired or
wireless networks and both can degrade the accuracy of the positioning estimation. Many
of these interferences cannot be eliminated; therefore, it is important for an indoor posi-
tioning system (IPS) to find a way to minimize interference affects [26].
There are many benefits to creating a positioning system that works indoors. Such a sys-
tem could be used for navigation in a mall, locating firemen in an enflamed building,
locating patients and equipment in a hospital, and identifying proper x,y coordinates in
construction. Although misleading, an indoor positioning system isn’t limited to the in-
doors. An IPS could be used outdoors to help boost GPS reception. This could be used
in places with a lot of interference such as a tunnel, where the GPS is blocked, or a large
city with the tall buildings causing multipath effects.
12. 2
1.1 Signal Measurement Methods
The first step in localization is the signal measurement method. This represents what in-
formation is gathered from the mobile device and how. Our focus here is on the three
most popular methods. These include time based methods, angle based methods, and re-
ceived signal strength based methods.
1.1.1 Time Based Methods
The two most common time based methods are time of arrival (TOA) and time difference
of arrival (TDOA).
Time of arrival deduces the distance between the transmitter and receiver using the delay
between the signal being sent and arriving. This can be done because the distance between
the transmitter and receiver is directly proportional to the propagation time, the time spent
by the signal traveling from receiver to transmitter. Below is the equation used to deter-
mine the distance between transmitter and receiver R, where t is the propagation time, and
𝑐1 is the speed of the signal, generally a known constant [42].
𝑅 = 𝑡 ∗ 𝑐1
The time the signal spent traveling can be found by comparing a time stamp sent out by a
transmitter and deducting it from the time the signal was received. For this method to
work properly, it is imperative that the internal clocks of the transmitters and receivers are
perfectly synchronized [32].
TDOA and TOA operate similarly, however TDOA does not require the transmitters and
receivers to be perfectly synced. TDOA only requires that the receivers are synced with
one another; this allows new mobile devices to use the positioning system with little hassle
[42]. The receivers only need to be synced with one another because instead of using the
travel time of the signal between transmitter and receiver like in TOA, the time that each
signal arrived at the receivers is used [42]. The arrival times from different receivers can
be compared and the time difference between them can be found. The TDOA of a signal
13. 3
between two receivers will result in a hyperbola along which the transmitter must lie, Fig-
ure 1.
Figure 1: Location hyperbola created by two receivers
When an additional receiver is included, three hyperbolas are created, Figure 2, and the
location of the mobile device must lie at the intersection of these three hyperbolas.
Figure 2: Location hyperbolas created by three receivers
The number of hyperbolas continues to increase in a triangular number sequence (i.e. 1,
3, 6, 10, 15, 21…) with the addition of an extra receiver. The accuracy of the system
continues to increase up to 6 receivers. At this point the small increase of accuracy no
longer outweighs the extra calculation time needed [18].
14. 4
It is also possible to use TDOA with only one receiver as long as the transmitter sends two
different kinds of signals. The distance between the transmitter and receiver, R, can be
found using the equation below where 𝑡1 and 𝑡2 are the propagation times of the different
signals, and 𝑐1 and 𝑐2 are the speeds
𝑅
𝑐1
−
𝑅
𝑐2
= 𝑡1 − 𝑡2 [42]
The downside of using TOA or TDOA is that they both require at least three access points
to be effective and depend on line of sight (LOS) between the transmitter and receivers.
This means that for optimal performance the transmitter and receiver must be able to see
each other and be able to transmit the signal in as direct of a line as possible. This isn’t
always ideal in an indoor environment where there are often times many obstacles between
a receiver and transmitter [32].
1.1.2 Angle Based Methods
The most popular angle based method is called angle of arrival (AOA) or direction of
arrival (DOA). In a system that uses AOAs, the receivers have the capability of measuring
the angle of arrival based on information sent. Techniques like angle diversity are often
used to exploit the directionality of the receiver. Direction finding can be accomplished
with a directional antenna or an array of antennas [10]. Antenna arrays use the difference
in arrival times of an incoming signal throughout the different antennae elements to deter-
mine the angle information of the signal. No time synchronization is needed to use angle
based methods, however angle based methods are highly sensitive to multipath and can
become inaccurate as distances increase [10].
1.1.3 Received Signal Strength Methods
Received signal strength (RSS) methods are performed by analyzing the attenuation in-
troduced by the propagation of the signal from transmitter to receiver. There are two ways
to use RSSs. They can either be stored and used later for fingerprinting or they can be used
immediately to get a distance estimate between a transmitter and receiver. To attain a
distance estimate, the difference between the transmitted signal and the received signal
15. 5
must be analyzed [20]. By comparing many estimates, the location can be determined.
However, due to multipath effects, the RSS method is site specific. This means that a site
survey needs to be performed at any location RSSs are to be used. Received signal
strengths do not have a LOS or clock synchronization requirement which makes them
useful in complex indoor environments. RSS is an integral part of scene analysis as well
as triangulation and will be covered in later paragraphs [20].
1.2 Location Estimation Techniques
The second step in localization is finding the physical location of the target. This can be
done by using different location estimation techniques. These techniques use the infor-
mation gathered by the signal measurement methods along with the known location of the
reference nodes to identify the location of the target. There are many location techniques
but only three will be covered in depth: triangulation, scene analysis and proximity.
1.2.1 Triangulation
Triangulation is a location technique that uses the geographic properties of triangles to
estimate the location of the target. There are two types of triangulation commonly used,
trilateration and angulation. The trilateration technique estimates the position of the target
by measuring distances to reference points with known locations [13]. As demonstrated
in Figure 3, if the geographical locations for A, B and C are known, (x1,y1), (x2,y2) and
(x3,y3), the absolute location of E, (x,y) can be determined by using the lengths of R1, R2
and R3 [42].
Figure 3: Triangulation Example [13]
16. 6
R1, R2 and R3 can be found using the following equations:
(x1-x)2
+ (y1-y)2
= R1
2
(x2-x)2
+ (y2-y)2
= R2
2
(x3-x)2
+ (y3-y)2
= R3
2
[42]
Once R1, R2 and R3 are determined, the location of E becomes clear by following the
subsequent steps. If the distance R1 is all that is known, then the location of E must lie
somewhere on circle A with radius R1. Once the distance between the target and a second
point is known, R2, the location is now narrowed down between the intersections of circles
A and B. When a third distance is introduced, R3, the location of E can only be in one
spot, the intersection of circles A, B and C [42]. This technique can be used for localiza-
tion in a third dimension by adding a fourth point. However, in a real system there is
always error and the three circles will most likely not intersect. Therefore it is important
to take multiple measurements to help minimize the inaccuracy. Despite this, trilateration
is generally the most accurate of the locating techniques because it can deal with multipath
effects well. However, it is also generally the most complex system to implement [13].
1.2.2 Angulation
The second type of triangulation, “angulation”, does not require the measurement of dis-
tances, it requires measurement of angles instead. Angulation uses AOAs to estimate the
location of an object based on the intersection of several pairs of angles [10]. As can be
seen in
Figure 4, as long as the locations of A and B are known, the angles between access points
and the object being located can be used to determine the location of said object [42].
17. 7
Figure 4: Angulation Example [42]
An advantage of AOA is that only two receivers are needed to estimate a 2D location and
only three are needed to estimate a 3D location. Despite the advantage of only needing a
few receivers, the hardware required can become very expensive [42]. Not only that, but
there is large estimate degradation as the mobile target moves further away from the meas-
uring units. Minor angle errors are insignificant over a small distance, but at a large
distance, the small angle error can create significant location errors. The quality of the
estimate is dependent on the accuracy of the angles measured, therefore it is important to
try to eliminate any thing that may reduce this accuracy. Problems can occur from shad-
owing and multipath effects [10].
1.2.3 Scene Analysis
A second technique used for IPSs is scene analysis, also known as location fingerprinting.
Scene analysis is a technique that takes premeasured location related data called finger-
prints and estimates the location of the object by comparing real time location related data
to these fingerprints [5]. The data primarily used for this technique are RSS measure-
ments. Scene analysis has two phases: the offline phase and the online phase.
1.2.3.1 The Offline Phase
The offline phase comes first and involves a site survey for the environment that the sys-
tem will be installed in. During the site survey, RSS measurements are recorded
throughout the environment from nearby measuring units, this is called profiling [13].
These measurements are stored along with the location they were found for later use. The
offline phase yields better results if multiple samples of each fingerprint are taken and
normalized. When the site survey is complete, all the data is compiled in one location. A
18. 8
visual representation for a 1D and 2D compilation of fingerprints for a single measuring
device are included below as Figure 5 and Figure 6, respectively [5].
Figure 5: 1D RSS Fingerprint
The y axis is unitless in Figure 5 because the values were chosen arbitrarily to showcase
what a 1D compilation would look like. Figure 5 shows the RSS values at ten 1D loca-
tions. The second phase of scene analysis involves interacting with this data. For
example, it appears that an object that receives an RSS value of 48 from this access point
would probably be at location 5 while an RSS value of 6 would probably be at location 4.
However, a value of 23 would not as easy to identify. The object could be at location 6,
8, 9, or 10. Examples like this are not uncommon, which is why it is important to have
multiple access points. The fingerprints of location 6, 8, 9 and 10 may look the same
according to this access point, but the fingerprint may be extremely different for another
access point. Figure 6 highlights how the data can become increasingly complex in a 2D
environment.
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10
ReceivedSignalStrength
Location
19. 9
Figure 6: 2D RSS Fingerprint
Due to the complexity of the data in a 2 or 3D environment, it is important to choose how
accurate and precise the IPS should be. Location accuracy is reported as the distance
deviated from the actual position and is usually given in units of length. Location preci-
sion is reported as the amount of position information that are within the given distance
accuracy and is given as a percentage. The easiest way to determine the accuracy and
precision of a system is to break it down into small parts, the extent of which is called
granularity [20]. For example, an IPS with a granularity of one meter would be able to
determine the correct location of an object within a square meter (or cube if 3D) a certain
percentage of the time. When measuring the performance of a system, the grain size is
the lowest bound of accuracy of a system. A system with a grain size of 1 m can be
accurate within 1 m, 2 m 3 m, or any other factor of 1 m. However the accuracy of a
system means nothing without listing the precision. A normal listing of performance
would state the system as being accurate within a distance a certain percent of the time,
for example a system may be accurate within 1 m 70% of the time. The precision of a
system is almost always directly related to the accuracy. This means that the larger the
area the estimate covers, the more often it will be correct. This can become important
when deciding the goals of a system. If the accuracy is too high, the precision might be
100%, but the data would be worthless. On the other extreme, if the accuracy of a system
1
2
3
4
5
6
7
8
9
10
0
20
40
60
80
1
3
5
7
9
Y Location
ReceivedSignalStrength
X Location
20. 10
is too low the results might be particularly imprecise, once again leading to useless data.
It is possible for a system to become more precise without losing accuracy, but it comes
at a cost. These costs often include money, latency, or size of the system.
The size of granularity helps determine the amount of fingerprints that need to be collected
during the site survey. Whether the granularity needs to be on the magnitude of meters or
centimeters often depends on the system being designed for. If the system is being de-
signed to track which room a mobile device is in, a granularity of 3 meters is generally
acceptable. However, if a system is being used to aid in the construction of a skyscraper,
3 meters would not work.
To minimize calculation time, it is ideal to take RSSs in a rectangular fashion to make
extrapolation easier. A good way to do this is to create a rectangular grid of points over-
lapping a map of the environment. Each grain should be able to contact at least three
access points. Once every location on the grid has its fingerprint take, the offline phase is
complete [16].
1.2.3.2 The Online Phase
As stated above, the second phase of scene analysis involves matching live RSS measure-
ments to the offline model to create accurate locations estimates. This phase is called the
online phase. There are many algorithms that can be used to help fingerprint a particular
signal, these include the nearest neighbor method, support vector machines, and many
other probabilistic techniques. However, the most commonly used technique is measuring
the signal distance between each vector and fingerprint in the database [16]. The signal
distance is most often calculated using the Euclidean distance, Z, which represents the
distance between the live RSS values and the stored RSS values. It is important not to
confuse signal distance with physical distance. Signal distance represents how close the
RSS measurements align with one another while physical distance represents how far an
object is from the receiver. The stored RSS value with the lowest signal distance has the
best match with the received RSS value. The Euclidean distance is defined below where
21. 11
p is an actual RSS measurement from the site survey and r is a live RSS value from an
individual access point [16].
𝑍 = ∑(𝑝𝑖 − 𝑟𝑖)
1
2
𝑁
𝑖=1
As mentioned above, the location with the lowest Z value is the closest match. One close
match does not guarantee the location of the mobile device. This is why it is important to
have many access points. A study performed by Kaemarungski, Figure 7, shows that the
precision of the results do not significantly increase after five access points are added to
the system. This makes five a good balance between price and precision.
Figure 7 Precision vs. Number of Access Points [16]
There are ways to determine the precision of a system before it is fully implemented to
help determine whether a location will be a good fit for scene analysis. One way is to
analyze the standard deviation of the RSS data recorded. The larger the standard deviation
of the RSS data, the less precise a system will be. A large standard deviation means that
the results are more likely to be further away from the mean and create false positives.
The standard deviation of the data relies on the environment of the system and cannot be
fully controlled. It can, however, be minimized by increasing the RSS samples taken and
analyzed at a given time. It must be kept in mind that increasing the samples taken does
22. 12
increase the estimation delay. The standard deviation of an environment can sometimes
be predicted without even taking fingerprints. The data for a building with a lot of open
space will generally have a large standard deviation of RSS data while a building with
small closed spaces will generally have a small standard deviation of RSS data [16].
Another way to determine whether an environment will be a good fit for scene analysis is
to look at the path loss exponent, α, of the area. Similar to the standard deviation of the
data, the path loss exponent is controlled by the environment. Figure 8 shows the precision
of a system increasing as path loss exponent increases [16].
Figure 8: Precision vs. Path Loss Exponent [16]
The precision increases with path loss exponent because it represents the attenuation of a
signal over distances. Therefore, an environment with a large path loss coefficient will
yield a compilation of more diverse fingerprints. This is because small location changes
return varied RSSs making fingerprints easier to distinguish. The more varied a set of
fingerprints, the higher the precision of a system [16].
23. 13
1.2.4 Proximity
The last and simplest technique that will be discussed is proximity. Proximity detection
provides symbolic relative location information. The position of the transmitter is deter-
mined when a receiving antennae, with known position and limited range, detects the
presence of the transmitter. Since the range of the antennae is limited, it is known that the
transmitter lies somewhere within that range. In Figure 9, E2 and D lie in the proximity
of the sensor while E3 does not.
Figure 9: Proximity Example [13]
If two antennae pick up the signal, the location is determined to be closest to the antennae
that received the strongest signal. Since this method is simple, it can be implemented by
many different media, in particular, systems using infrared, RFID, cell identification or
cell of origin [20]. This method is often associated with tracking cell phones to the nearest
cell tower. The accuracy of these methods depends on the density of antennae and the
signal range. The advantage of proximity is that it can be used with nearly any type of
existing radio infrastructures. The system is also very cheap because the targets need only
emit an identification code. The disadvantage is that the precision is limited by the range
of the sensors and the location of the targets [13].
1.3 Mediums Used
This section introduced the signal technologies that are commonly used. The signal tech-
nology for a particular system can have a heavy impact on the accuracy of the positioning
estimate.
24. 14
1.3.1 Radio Waves
Radio frequency is the most expansive of the mediums used for IPSs. The advantage of
using radio waves is that they have the ability to travel through walls and human bodies.
This allows them to have a larger coverage area [13]. The extensive coverage area helps
reduce the amount of hardware necessary, driving down costs. These reasons have led to
radio waves being integrated in many technologies that are used on a daily basis such as
WiFi or cell phone service. The widespread use of RF makes it a good choice for IPSs
because often, previously installed infrastructure can be used, saving cost and implemen-
tation time. Some of the technologies that currently use RF waves that can be adapted for
an IPS are radio frequency identification (RFID), wireless local area networks (WLAN),
wireless personal area network (WPAN or Bluetooth), and ultra wideband (UWB) [13].
1.3.1.1 RFID
Radio Frequency Identification (RFID) is a means of storing and retrieving data wirelessly
through the use of electromagnetic fields. A tag is used to store the information and can
either be powered at short range by the reader via induction (passive) or they can contain
a battery themselves (active) [30]. Passive RFID tags are small and inexpensive; however,
they can only operate over a very small range. Active tags are larger and more expensive
but can operate over a much larger area making them a better fit for IPSs. Both types of
RFID tags do not require line of site. RFID tags are excellent at identifying people but
require a large infrastructure investment to become useful as an IPS [30].
1.3.1.2 WLAN
WLAN is most often referred by the standard that governs it, WiFi. Since WiFi is so
ubiquitous in the world today, WLAN is an extremely popular way to implement an IPS.
One of the reasons for its popularity is that the infrastructure already exists; all that is
needed is a system that can take advantage of wireless signals. However, using WLAN
for RSS can be hard because the signal strength is affected by many things such as other
access points, movement, orientation of the device, and obstacles such as walls and doors
25. 15
[21]. WLAN is a great low cost positioning technology but the complex indoor environ-
ments can limit the accuracy to several meters. WLAN can also become costly if a large
number of users use the positioning system at once [21].
1.3.1.3 WPAN
WPAN is similar to WLAN in that it is also commonly referred to by the standard that
regulates it, Bluetooth. Bluetooth operates using the master/slave protocol and according
to Bluetooth 2.0 standards, can have a range of up to 100 m. Bluetooth is another tech-
nology that is inexpensive because it is already in many devices. Bluetooth has its
downsides; Topaz [13], an IPS system that runs on Bluetooth, can only minimize the error
range to around 2 m. This is barely sufficient to provide room level accuracy. Therefore,
like many Bluetooth systems, another medium had to be implemented in order to improve
on the accuracy. The Topaz system introduced IR and increased the accuracy to 1 m.
However, introducing another system can add a delay in the system. For Topaz, the delay
that was added was about 10 to 30 seconds to calculate the location [20].
1.3.1.4 UWB
Ultra Wideband RF has an advantage over the other RF IPSs because it does not suffer
from multipath issues. This is because UWB pulses at 1 ns which makes it possible to
filter the reflected signal from the original signal. This greatly improves the accuracy of
an IPS and makes it an ideal system for TOA. Not only that, but UWB does not have a
LOS requirement, has a high penetration ability, and is relatively cheap [13].
1.3.2 Infrared
Infrared is another medium that can be used for indoor positioning systems. It is popular
because many systems that are used today already have infrared capabilities. These tech-
nologies include, but are not limited to, TVs, printers, and mobile phones. Using infrared
requires LOS and is limited to a per room basis [42]. The sensors are small and lightweight
and the infrastructure is not very complex. These systems do not score highly on the
privacy issue. IR systems are highly accurate but do not react well to light pollution in
the form of lights bulbs or even sunlight. There are ways to eradicate some of the error
26. 16
caused by these sources; however, it involves installing a noise cancelling signal pro-
cessing algorithm. Such a system would cause the system to become more expensive and
increase the processing time. IR emitters are cheap but the infrastructure cost per area
covered is high because of the LOS requirement [13]. This requires the infrastructure to
be installed in every room. IR signals do not penetrate opaque materials such as clothing
well which means that the IR transmitter must always be visible. In order to work under
any orientation, the tag must always be oriented properly or more sensors must be installed
in the room [20].
1.3.3 Ultrasound
The systems that have been developed for ultrasound are inexpensive but have problems
dealing with interference. They generally need to be combined with other systems to re-
ceive desired results. The accuracy can be up to the centimeter level but reflected
ultrasound signals and other noise sources, especially high pitched sounds such as jingling
metal objects, potato chip bags, or foil can interfere with the results [20]. Ultrasound can
also be disturbing to pets or any other animals that can hear parts of the ultrasonic fre-
quency range.
1.3.4 Electromagnetic Waves
Electromagnetic waves can be used for IPSs. The systems have high accuracy and do not
require LOS. The sensors are small, robust, fast, and can track multiple sensors at a time.
While the sensors that are used to receive data from the trackers are cheap, the trackers
themselves are expensive [13]. They also require a lot of power and the batteries that are
currently being used in such systems do not last long. This type of system generally can’t
be used in an environment with many metal objects and can only operate within 3 m. Until
the range issue is addressed, electromagnetic waves are not an ideal medium for IPSs
[1213].
1.3.5 Sensor Networks
A sensor network is any system that uses sensors to help estimate position. A “sensor”
refers to any device that generates proportional outputs to environmental or physical con-
ditions such as sound, pressure, light, orientation, etc. There are two types of sensors,
27. 17
active and passive. Active sensors interact with their environments, such as radar, while
passive sensors only receive information that is already out there. The price of high quality
sensors is going down and the batteries powering them are becoming more efficient, how-
ever they still aren’t very accurate. They work best when paired with other mediums [13].
1.3.6 Audible Sound
Audible sound is a useful medium because many mobile devices already have the ability
to create it. However, there are a great deal of limitations to this type of IPS. Audible
noise is very sensitive to environmental sounds and can be interfered with in dynamic
indoor situations. It also doesn’t have very high penetration ability so it can generally only
be used in a single room. Not only that, but audible sound can annoy the users because
they can hear the sound being transmitted by their mobile device [22].
1.3.7 Visual
Vision based positioning systems work by identifying a person or device in a complex
indoor environment and tracking them. The advantages are that vision based systems do
not require any type of tag or transmitter to be carried by the user and that a low cost
camera can be used to cover a large area. A vision based system is best for giving symbolic
locations but are not very good at identifying the exact spatial location of a person or
object. For example, vision based systems can identify person A sitting on the sofa and
watching TV, and other similar scenarios. A drawback to vision based positioning sys-
tems is that there is no privacy; a person cannot prevent themselves from being tracked.
The system is also not very reliable, especially in a dynamically changing environment.
This is because the estimations are based on a stored visual database, so changing the
location of something will affect the results. The image processing can be interfered with
in static environments too. This can happen if the lighting changes for any reason includ-
ing time of day or weather. The system also has issues tracking multiple sources; the more
sources being tracked the higher the computational cost [6].
28. 18
2. METHODOLOGY/APPROACH
Having identified the measurement methods, the location techniques and the current tech-
nologies, it is important to look into the performance metrics of the positioning systems
that are currently being used. For this paper, 28 IPSs were investigated. The goal was to
analyze these systems by looking at a variety of metrics applicable to indoor positioning
systems. The information was collected first hand by communicating with some of the
creators of the systems and by evaluating papers written by them. The information was
summarized in Appendix A and was sorted under the following headings: name, type,
LOS requirement, signal measurement method, localization frequency, cost, performance,
robustness, complexity, scalability, human factors, and battery life. The information gath-
ered for Appendix A was used to analyze the data and create Table 1 and Table 2. The
goal was for a reader to be able to determine general information about the systems simply
by reading a row from left to right without looking at the headers. The information in the
tables was classified by the rules listed below:
Name: The given name of the system. If the name of the system was not given, a short
“uncapitalized” description will be listed.
Type: The mediums used by the system. If multiple systems are used, the primary me-
dium is listed first.
LOS: Whether or not the system requires line of sight. If the system requires line of
sight, it will be listed as “LOS”, if the system does not require line of sight, it will be listed
as “No LOS”.
Signal Measurement Method: The signal measurement method used to determine the
location of the object.
Localization Frequency: A measure of how often the location is calculated. Included in
this measurement is how long it takes for a system to calculate the location. “Short” will
be defined as 1 second or less and “Long” will be anything longer than 1 second.
29. 19
Cost: This is a loose definition on how expensive the system would be to implement. Cost
per square meter is not readily available without a site survey for any of these systems;
therefore, this classification will be based on the scenario of outfitting 10 rooms. If the
costs would be on the magnitude of tens of thousands, it would be “Expensive” if the cost
would be below that, it would be “Inexpensive”
Performance: How well a system performs. If a system is accurate 90% of the time at a
distance of 1 cm or lower, it will be listed as “High Accuracy.” If the system is accurate
90% of the time between 3 m and 10 cm it will be listed as “Room Level Accuracy”.
Anything above 3 m will be listed as “Inaccurate”
Robustness: How much interference a system receives and how well it is handled. If the
interference a system receives is minimal the system is “Robust,” if a system has a great
deal of interference it is “Not Robust.”
Complexity: How easy a system is to set up and use. If the installation time of the systems
infrastructure is short or if the infrastructure already exists and if the system is easy to use,
it will be listed as “Simple.” If the system is hard to use or set up or the installation of the
infrastructure is time consuming, the system will be listed as “Complex”
Scalability: How well a system scales to a large environment. If the system can be scaled
to a large environment with multiple users without becoming costly, it will be listed as
“Scales Well”. If the system becomes cost prohibitive with a large environment or multi-
ple users, it will be defined as “Scales Poorly”.
Human Factors: Whether or not there are any human factor issues. This will apply to
the tag. Is it wearable? Does it have to be visible? Does it make a sound? Is it wireless?
Lightweight? Small? These issues will be taken into account when a system is labeled
either “Convenient” or “Inconvenient”
30. 20
Battery: How long the battery life of the system lasts for. The battery life will be meas-
ured in either “Hours”, “Days”, “Months” or “Years”. A system that is rechargeable will
be classified under the “Days” category.
31. 21
3. RESULTS AND DISCUSSION
In this section, the existing systems are classified into several groups in order to make a
comparison against them. The results from the analysis are listed in Table 1 and Table 2
and are discussed in depth below.
3.1 Results
Table 1: Survey Results
Name Type LOS
Signal Measurement
Method
Location Fre-
quency Cost Performance
Active Badge [37] IR Yes RSS Short to Long Inexpensive Room Level
Firefly [12] IR Yes N/A Short Expensive High Accuracy
OptoTrak [25] IR Yes N/A Short Expensive High Accuracy
IRIS_LPS [2] IR Yes Image Processing Long Inexpensive High Accuracy
Active Bat [1] Ultrasound Yes TOA, RSS Short Expensive High Accuracy
SmartLOCUS [2], [9]
Ultrasound/
WLAN No TOA plus RSS Short Expensive High Accuracy
Cricket [28], [29] Ultrasound/IR Yes
TDOA between Ultra-
sound and IR, RSS Long Expensive High Accuracy
Sonitor [33]
Ultrasound, WiFi
and RFID No N/A Short Inexpensive Room Level
WhereNet [38] RFID No UHF TDOA Long Inexpensive Inaccurate
SpotON [15] RFID No RSS Short Inexpensive Inaccurate
LANDMARC [26] RFID No RSS Short Inexpensive Room Level
RADAR [7],[8] WLAN No RSS, Fingerprinting Short Inexpensive Inaccurate
Ekahau [11] WLAN/RFID No RSS Short Inexpensive Room Level
COMPASS [17] WLAN No RSS, Fingerprinting Short Inexpensive Room Level
Horus [39], [40] WLAN No RSS Short to Long Inexpensive Room Level
neural system [4] WLAN No RSS Short Inexpensive Inaccurate
robot based [18] WLAN No RSS Short Expensive Room Level
MultiLoc [27] WLAN No RSS Long Inexpensive Inaccurate
TIX [14] WLAN No RSS N/A Inexpensive Inaccurate
SELFLOC [14] WLAN /Bluetooth No Fingerprinting Short Inexpensive Room Level
Topaz [34] Bluetooth/IR No RSS+IR Long Inexpensive Room Level
OPT [3] Sensor Networks No RSS Short to Long Inexpensive Room Level
Ubisense [35] UWB No TDOA plus AOA Short Expensive High Accuracy
Sapphire Dart [41] UWB No
Unidirectional UWB
TDOA plus AOA Short N/A Room Level
MotionStar [24] Magnetic Yes
Pulsed DC magnetic
fields Short Expensive High Accuracy
Easy Living [6] Visual Yes Image Processing Long Inexpensive Inaccurate
Beep [22] Audible No TOA Short Inexpensive High Accuracy
SnapTrack [23] Assisted GPS Yes TDOA Long Expensive Inaccurate
32. 22
Table 2: Survey Results Continued
Name Type Robustness
Infrastructure
Complexity Scalability Human Factors
Power Con-
sumption
Active Badge [37] IR Not Robust Complex Scales Poorly Inconvenient Years
Firefly [12] IR Not Robust Simple Scales Poorly Inconvenient N/A
OptoTrak [25] IR Robust Simple Scales Well Convenient No Battery
IRIS_LPS [2] IR Not Robust Simple Scales Poorly No Battery
Active Bat [1] Ultrasound Not Robust Complex Scales Poorly Convenient Months
SmartLOCUS [2], [9]
Ultrasound/
WLAN Robust Complex Scales Poorly Inconvenient Days
Cricket [28], [29] Ultrasound/IR Robust Complex Scales Well Convenient Hours
Sonitor [33]
Ultrasound, WiFi
and RFID Robust Complex Scales Well Convenient Years
WhereNet [38] RFID Robust Complex Scales Poorly Convenient Years
SpotON [15] RFID Robust Complex Scales Well Convenient Hours
LANDMARC [26] RFID Robust Complex Convenient Years
RADAR [7],[8] WLAN Robust Complex Scales Well Convenient Days
Ekahau [11] WLAN/RFID Robust Simple Scales Well Convenient Days
COMPASS [17] WLAN Robust Complex Scales Poorly Convenient Days
Horus [39], [40] WLAN Robust Simple Scales Well Convenient Days
neural system [4] WLAN Robust Complex Scales Well Convenient Days
robot based [18] WLAN Robust Complex Scales Well N/A Days
MultiLoc [27] WLAN Robust Simple Scales Well Convenient Days
TIX [14] WLAN Robust Simple Scales Well Convenient Days
SELFLOC [14]
WLAN /Blue-
tooth Robust Complex Scales Poorly Convenient Days
Topaz [34] Bluetooth/IR Robust Complex Scales Poorly Convenient Days
OPT [3] Sensor Networks Robust Complex Scales Well Convenient Depends
Ubisense [35] UWB Robust Simple Scales Well Convenient Years
Sapphire Dart [41] UWB Robust Simple Scales Well Convenient Years
MotionStar [24] Magnetic Not Robust Complex Scales Poorly Inconvenient Hours
Easy Living [6] Visual Not Robust Complex Scales Well Convenient N/A
Beep [22] Audible Not Robust Simple Scales Well Inconvenient Days
SnapTrack [23] Assisted GPS Not Robust Complex Scales Well Convenient Days
3.2 Discussion
3.2.1 Infrared Results
The most prominent thing about the IR systems is that they all require LOS and don’t scale
well. This is because of the mediums inability to penetrate opaque obstacles. The majority
of IR systems have high accuracy; however, this is generally because the systems are often
33. 23
used for mapping movement instead of actually locating an object or person in a room.
There are many existing technologies that use IR which makes the infrastructure for an IR
system relatively simple. Ultimately though, requiring line of sight makes the system
inconvenient to use because any tag that is needed has to be visible. Looking at these
results, it can be seen that IR systems work best when paired with another medium.
3.2.2 Radio Frequency Based Systems
Systems that use radio frequency have a few things in common. They scale well because
the RF can cover large distances and penetrate opaque objects such as people and walls.
The infrastructure for most RF systems already exists; they are generally inexpensive and
are convenient because the system is often made available through user’s mobile device.
Among the RF systems, a few things distinguish each medium. RFID systems are gener-
ally not accurate unless paired with another medium. WLAN systems all use RSSs to
analyze the data, have a short location estimation frequency, and provide room level ac-
curacy. Bluetooth systems are unable to predict accurate location estimates on their own
and are often paired with another medium. UWB systems use TDOA and AOA instead
of RSS. This means that UWB systems will not require as much maintenance as a system
using scene analysis. All RF systems are robust, but the ability of UWB systems to elim-
inate multipath effects makes them more robust than any other system.
3.2.3 Results for Systems Using Multiple Mediums
Systems that use multiple mediums were never inaccurate in this study. This is because
the mediums that are chosen in a multiple medium system are generally complimentary.
Where one medium fails, the other medium picks up its place, this is why none of the
multiple medium systems require line of sight. Implementing multiple mediums comes
at a cost though; multiple medium systems are among the most complex systems to install.
Other characteristics such as location frequency, cost, and scalability are generally repre-
sentative of the individual mediums that make up the system.
3.2.4 Other Medium Results
Sensor networks, magnetic fields, audible sound, and assisted GPS techniques cannot be
properly investigated due to lack of IPSs in this study that use these mediums. However,
34. 24
some assumptions can be made. Sensor networks will generally be complex and scalable,
magnetic systems will not be robust due to issues with metal, audible noise will always
have some human factor issues and assisted GPS will always have performance problems
and involve a large infrastructure cost.
3.2.5 Other Results
There are some other conclusions that can be drawn simple by looking at the data. For
example, it can be seen that RSS techniques generally have a short location frequency, are
inexpensive, offer room level accuracy, are robust, and are convenient to use. Any system
that uses RSS for fingerprinting will be more costly because the time to perform the offline
stage will be time consuming. Also, fingerprinting systems aren’t always robust, this can
be due to the need for many site surveys in a dynamic environments.
It can be seen that time and angle based methods offer the highest accuracy but are also
some of the more complex systems.
Some final results that can be seen from this data: the high accuracy systems are generally
expensive and vice versa. They are not very robust, are inconvenient to use, and often
require LOS.
35. 25
4. CONCLUSIONS
In this paper, a brief description of signal measurement methods, location techniques, and
commonly used mediums are discussed with many state-of-the-art localization technolo-
gies that currently exist. It was shown that each method, technique, and medium has its
own advantages and disadvantages. Different performance measurement criteria for the
state-of-the-art technologies are discussed and several tradeoffs are observed. A common
tradeoff was between accuracy and price.
Despite increased research in this field, there is no IPS that is the best for every situation.
The goal of the compilation in this paper is to help offer the right technology for the right
situation. When choosing an IPS, it is important to decide what qualities are most desira-
ble. For example, the qualities that are desirable for warehousing may be different than
those that are desirable for robotics or for firefighting.
Some factors that can help differentiate IPSs include the cost, how accurate the system
must be, how often the position must be estimated, how well the system needs to deal with
interference, how complex the system infrastructure can be, whether or not the system
needs to scale well, or whether a system needs to be user friendly. The systems that often
perform the best are the systems that use multiple methods, techniques, and mediums.
This is because the strengths of one part of the system can make up for the weaknesses of
another part of the system.
36. 26
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APPENDIX A: TECHNOLOGY SPECIFIC DATA
Name Type LOS Algorithm
Location
Frequency Cost Performance Robustness Complexity Scalability Human Factors
Power
Consumption
Active Badge
(1992) IR Yes RSS
Can
choose,
1/10 s to 15
s Low
Room level ac-
curacy, about
6m
Influence from
light source Low
IR does not
scale very well
Badges lightweight, IR
so must be worn outside
clothing, problems with
light sources
Battery life lasts
between .5 and 1
year
Firefly IR Yes N/A 3 ms
32 tags
$27,500
Error range be-
low 3.0 mm
Influence from
light source Low
Not reasonably,
also mostly to
track movement
not location
Tags are wired to a tag
controller N/A
OptoTrak IR Yes N/A Short Expensive
.1 mm to .5 mm
with 95%
Handles interfer-
ence well Low
Scales well/3D,
this is mostly to
track movement
not people
Small and lightweight
tags
No battery, emit-
ters are wired and
connected to the
power supply
IRIS_LPS IR Yes
Image Pro-
cessing Not fast Low 8-16 cm
Only static, cannot
locate dynamic Low
Many cameras
to install Small tags No battery
Active Bat Ultrasound Yes TOF, RSS
50 per sec-
ond Expensive
3.0 cm at 95%
in 3D
Influenced by re-
flection and
obstacles between
Needs a lot of
sensors attached
to the ceiling
Unrealistic, but
yes, but needs a
sensor every
1.2m No big issues, 15 months
SmartLOCUS
Ultrasound/
WLAN No TOF + RSS 1 second Expensive
2-15 cm, 50%
within 15 cm Good Medium Good/2D
Must attach a sleeve to
mobile unit, self-assem-
bling network
Same battery life
as the WiFi ena-
bled device used
Cricket Ultrasound/IR Yes
TDOA between
Ultrasound and
IR, RSS 4 seconds Expensive 10 cm Good Low Good Tags
Object receiver
consumes a lot of
power because it
does calculation
Sonitor
Ultrasound,
WiFi and
RFID No N/A
millisec-
onds Inexpensive
Room Level,
about 1 ft
Hidden targets can
be tracked
Needs numer-
ous detectors,
easy to install Any size
Lightweight, can be at-
tached to ID card 5 years
WhereNet RFID No UHF TDOA 3 s to 1 hr Low
2m to 3m, 50%
within 3m Good Complex Good/2D Lightweight 7 years
SpotON RFID No RSS Short Low
Depends on
cluster size Good Medium 3D Lightweight 10 hours
LANDMARC RFID No RSS 100 ms Low 50% within 1 m
Environmental dy-
namics easily
accommodated Medium
Tradeoff be-
tween accuracy
and density Lightweight 3-5 years
RADAR WLAN No
WLAN, RSS,
Fingerprinting 3 – 15 ms Low
2.5 m at 50%,
90% within
5.9m, accuracy
3-5m Good
Taps into exist-
ing WLAN
infrastructure Good/ 2D,3D Any WLAN device
Any WLAN de-
vice
Ekahau WLAN/RFID No WLAN RSSI Short Low
1 m, can track
thousands of de-
vices, 50%
within 2m Needs 3 APs
Uses existing
infrastructure,
needs site map-
ping Good/ 2D
Any WiFi device, small
lightweight
Rechargeable
tags
40. 30
COMPASS WLAN No
RSS, Finger-
printing
Set at 250
ms Inexpensive
Less than 1.65
m
Works through hu-
man body by
detecting orienta-
tion of device
Uses existing
infrastructure Good/2D
End user needs digital
compass, error when
close to electromagnetic
objects
Same battery life
as the WiFi ena-
bled device used
Horus WLAN No WLAN RSS
Latency
Accuracy
trade off Low
2m accuracy,
90% within 1.4
m Good Moderate Good/2D
Can use any WiFi ena-
bled device
Same battery life
as the WiFi ena-
bled device used
Neural IPT WLAN No WLAN RSS 1 second Low
3m, 90% within
5.12 using
SVM, 90%
within 5.40m
with MLP Robust Moderate Good/ 2D, 3D
Can use any WiFi ena-
bled device
Same battery life
as the WiFi ena-
bled device used
Robot based WLAN No RSS
3-6 per sec-
ond Medium
50% within 1.5
m Good Medium Good/2D N/A
Experimental,
just used a com-
puter
MultiLoc WLAN No RSS
Over 1 sec-
ond Medium
4 APs needed
for less than 20
ft Good Low Good/2D
Can use any WiFi ena-
bled device
Same battery life
as the WiFi ena-
bled device used
TIX WLAN No RSS N/A Medium
50% within 5.4
m Good Low
Good/2D, 3 APs
per 25-40m
Can use any WiFi ena-
bled device
Same battery life
as the WiFi ena-
bled device used
Selective Fusion
Location Estima-
tion (SELFLOC)
Wi-Fi & Blue-
tooth No
Fingerprinting,
heterogeneous
sensor fusion
(Wi-Fi+BT),
multi-algo-
rithms
(trilateration, K-
NN, Smallest
Polygon)
Sub-sec-
ond, that is,
in hundreds
of millisec-
onds Low
Accuracy is
within 10 feet,
precision is
about locating
sizes of a person
or smaller, cov-
erage area
depends on
placement of
Wi-Fi APs
Main cause of er-
ror is multi-path
(due to indoor
structures), sensor
fusion and time av-
eraging (filtering)
improve robust-
ness
Requires train-
ing, which is not
too heavy com-
putationally but
requires manual
labor
Not too scala-
ble, due to floor
planning and re-
calibration
issues
Requires manual train-
ing/calibration
Same as mobile
terminals or lap-
tops
Topaz Bluetooth/IR No RSS+IR
15 – 30 sec-
onds Medium
2m – 3m at 95%
room level
Uses Bluetooth
and IR to become
more robust
Many IR APs
need to be in-
stalled
Needs nodes
every 2-15 m
Tags need to be re-
charged each week 1 week
OPT
Sensor Net-
works No RSSI
Depends on
the sensors Cheap
25% within .6
m, 75% within
1.7m.
Error range 1.5
m to 3.8 m
Needs 3 sensors to
locate target, high
because uses other
sensors to help Low
Depends on
sensors but
could be
good/3D Small and lightweight Short
Ubisense UWB No
UWB TDOA
plus AOA Short
5 tags, 4
sensors,
$18,354, so
medium
high ish
15 cm in 3D,
99% within .3m Robust
Low, only infra-
structure needed
are sensors and
4 can cover 400
m^2
2-4 sensors per
cell (100-
1000m) needs
one tag per ob-
ject/ 2D,3D Small lightweight 1 year
Sapphire Dart, by
Zebra UWB No
Unidirectional
UWB TDOA +
AOA
200 times a
second N/A 1 ft
Robust, plus tags
can be dropped Low yes Small lightweight 7 years at 1 Hz
41. 31
MotionStar Magnetic Yes
Pulsed DC mag-
netic fields Short
Expensive
Trackers 1 cm
Doesn’t work
around metal
Small coverage
area, 3 m No
Sensors connected not
connected via wire any-
more, transmitter are
heavy, this is not really
IPS, more for biome-
chanics 1-2 hours
Easy Living Visual Yes
Image Pro-
cessing
Over a sec-
ond Inexpensive
Cannot guaran-
tee due to
interference,
mostly to find
what room you
are in
Does not work in
dynamic environ-
ment
Low, two cam-
eras can cover
an entire room Yes No device N/A
Beep Audible No TOF Short Inexpensive
.4 cm with 90%,
2ft 97% of the
time
Influenced by
sound sources Low Good/3D
Can use own mobile de-
vice
Your mobile de-
vice
SnapTrack Assisted GPS Yes TDOA
1-10 sec-
onds Expensive
5m-50m, 50%
25m
Poor, GPS needs
perfect conditions High Good, 2D, 3D
Can use own mobile de-
vice with GPS
Can use own mo-
bile device, no
incremental
power drain
