(PDF) Overview of Current Indoor Positioning Systems

Running head: INDOOR POSITIONING 1
Information technology - indoor positioning
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INDOOR POSITIONING 2
Introduction
In this research it is about the extent of the reliability of the multiple sensors fusion for
the indoor positioning. The positioning systems are used in locating numerous objects in the
world. One of the location objects is the GPS. GPS is the system of the satellites radio
transmitter which orbit on earth in great numbers; the main objective is to pinpoint the precise
place to people or even virtually any the vessel which are built with a receiver transmitter which
is within a small radius (Paul & Sato, 2017). It is therefore important to find an accurate as well
as reliable system for the indoor positioning system.
Indoor Positioning System has become the major sources to the accurate location
information in the world (Paul & Sato, 2017). Having reliable as well as accurate location
information could assist in the emergence of the services, recreation, tracking, navigation as well
as networking (Chen & Vadde, 2012). Several indoor positioning approaches have been
suggested such as, radio frequency, fingerprinting approaches, motion sensor based pedestrian
dead reckoning approaches and visual sensor (Chen & Vadde, 2012). Due to this aspect there are
numerous supplementary techniques which have been utilized such as Bluetooth, cellular,
wireless internet as well as Radio Frequency ID to provide positioning to the indoor setting
where the GPS do not function (Galván-Tejada, Carrasco-Jimenez & Brena, 2013). Reliability
specifies how often the system could attain the accuracy which is the range across multiple
locations (Jiaxing, 2017). As the positioning is the continuous real world process, the claimed
accuracy is not all the time consistent to the actual accuracy at any given location or time. For
instance, the performance of the performance of the consumer Grade GPS Receivers it could
depends on the canopy cover as well as the availability that results to the variance in accuracy
when it is in different setting (Jiaxing, 2017). According to the Skyhooks hybrid positioning
system that combines the GPS, cellular signals as well as WiFi highlights that the core engine
has an accuracy of ten meters and compare this kind of accuracy level to the GPS and A-GPS at
ten and thirty meters.
Reliability specifies how often the system could attain the accuracy which is the range
across multiple locations (Jiaxing, 2017). As the positioning is the continuous real world
process, the claimed accuracy is not all the time consistent to the actual accuracy at any given
location or time. For instance, the performance of the performance of the consumer Grade GPS
Receivers it could depends on the canopy cover as well as the availability that results to the
variance in accuracy when it is in different setting (Jiaxing, 2017). According to the Skyhooks
hybrid positioning system that combines the GPS, cellular signals as well as WiFi highlights that
the core engine has an accuracy of ten meters and compare this kind of accuracy level to the GPS
and A-GPS at ten and thirty meters.
There is also aspect of accuracy which would be discussed in the research. Accuracy is
regarded as the localization error distance. This is the distance which is between the actual and
the calculated position (Li, Zhao, Ding, Gong, Liu & Zhao, 2012). The accuracy of the
positioning system could vary depending to the localization technique and algorithm which is
used. When choosing the implementing these techniques as well as algorithms, average of the
coverage, cost of implementation and the calculation accuracy should be balanced. To most of
the positioning technique, any increase in the accuracy is related to the additional power of

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processing, equipment that is required or the increased system latency (Khoshelham &
Zlatanova, 2016).
This research will discuss aspects related indoor positioning technique and algorithms
which are involved in localization. Some of these techniques usually involve various signal
system such as cellular, and Bluetooth. The most common indoor positioning systems rely to the
Wireless Internet signals from the routers (WiFi). While different algorithms utilizes signals in
calculating position in different ways to each of them have strengths, and unique variable which
impact on the accuracy (Li, Zhao, Ding, Gong, Liu & Zhao, 2012). For this research it would
focus on the Blue tooth, Wi-Fi and the Magnetic fingerprints. First it is important to define
various definitions to which are the main point of this paper these are reliability, and accuracy.
To undertake this research has been motivated by the interest in the research on the indoor
positioning technology. This field is diverse and not many individuals are away on some of these
technologies despite them used in every day applications.
This research is of importance since it would add the knowledge based on the current
indoor positioning techniques. Moreover, it would discuss on the issues which are related to the
localization techniques. The significance to find accurate and more reliable systems for the
indoor positioning system has been emerging from the shortage of the information, thus the
research will help lessen on the gap and provide comprehensive information in relation to this
field.
2.0 Literature review
A review to the existing literature is carried out to support on the research work which is
undertaken in this research topic. The review is about the existed research papers which would
examine on the current study to understand the aspect missing which would be filled by this
research. The literature is on different aspects within the research.
2.1 Global Positioning System
There are numerous researches that have been done on the GPS. GPS is the global navigation
satellite system to which decides the position to any target through measuring the propagation
holdup from the signals from satellites to GPS receiver (Xiao, Ni & Toh, 2011). In the recent
past, researchers have tested on wide array of systems to make an effort to develop on the precise
GPS signals to numerous applications that are under numerous traffic conditions. Lufeng Zhu et
al (2011) did an analysis to acknowledge on the need to have more effective GPS data
acquisition than the localized data collection that was generated from the traditional loop
detectors. In this study they analyzed the conventional fast acquisition, and fast acquisition of
GPS receiver aided and was offered INS information and signal was caught by spectrum
zooming. On another research Kazuyuki & Ka Cheok (1998) they performed on the fuzzy logic
Kalman filters sensor fusion technique. Within this study it was examined based on the
theoretical background for the sensor fusion depending on the Kalman filtering and fuzzy logic
scheme (Le Grand & Thrun, 2012). Validity to the technique was confirmed utilizing
experimental data from a real automobile navigation around the urban areas. The leads to this
pointed out approach to the automobile might be traced with high accuracy as well as
repeatability despite limitations to GPS. However, this research has not combined on the

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Acquisition and tracking method to the GPS and the LIC signals to produce the accurate
positioning system and through this research it would help fill that gap.
2.2 Indoor Positioning System (IPS)
The wireless indoor positioning system are program used to locate the objects or even
individuals who are inside the building through use of radio waves , acoustic signals , magnetic
fields or maybe other sensory information that is gathered by the mobile devices (Chen & Vadde,
2012). According to Bahl (2000) in their approach to indoor positioning system he used WLAN
signals to determine on the distance between the mobile device and the WLAN routers. This
concept for the mobile device location is similar the positioning module which was used in the
positioning algorithms (Khoshelham & Zlatanova, 2016). However, this method lacks the hybrid
framework which measures on the signal strength to the WiFi to position on the location to the
mobile device (Jiaxing, 2017). This research would help address on this issue through suggestion
since hybrid requires wireless connections to work while the system is only making the usage of
the Bluetooth connection to enhance on the precision. Lassabe (2006) has helped to present an
iterative technique to address on the issue of trilateration.
2.3 Localization Techniques
2.3.1 Received Signal Strength Indicator (RSSI)
There are many researchers who have used RSSI for the localization and they have discovered
on some techniques to deal with such kind of issues (Kubrak, Le Gland, He & Oster, 2009).
Mao, Fidan & Anderson, (2007) proposes some of the models which are used in optimizing the
RSSI value such as statistical model, correction and the Gaussian According to Wu et.al on their
research proposed on the probabilistic localization technique (Jiaxing, 2017). It highlighted on
this paper RSSI is impacted by the hardware orientation and other factors. RSSI when it is used
to create the location signatures are unreliable when it comes to multiple devices from the
various vendors and physical constraints which are used. According to Kubrak, Le Gland, He &
Oster (2009) to their research discusses that RSSI based on the localization is based on the loss
model to free on the space propagation and logarithmic path loss to radio signal. The research
however, does not address how the RSSI errors are measured.
Technologies for Localization
2.4.1 Wi-Fi
According to the related work on this aspect there are authors who introduced RADAR system
which relies on the Wi-Fi analysis. Xiao, Ni & Toh (2011) approach in their research has
attracted a lot of effort due to the properties such as using current infrastructure and resilience to
the multipath impacts compared to the traditional approaches (Galván-Tejada, Carrasco-Jimenez
& Brena, 2013). There are many researches who have demonstrated the positioning performance
on the Wi-Fi suffers from the problem such as the fast fading as a result to the interferences in
case the characteristics to the radio wave propagation is utilized in locating the mobile device
(Wang, Zhao, Luo & Lu, 2011).
2.4.2 Bluetooth

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Different research has been done on this aspect. Kwiecień, Maćkowski, Kojder & Manczyk
(2015) research on reliability of the Bluetooth technology for the indoor localization system on
the overview of the technologies used for the localization (Röbesaat, Zhang, Abdelaal & Theel,
2017). They focused on the localization techniques to the cellular networks and the WLAN
environments. Nonetheless the research does not describe signal processing technique useful in
the localization algorithm (Xie, Gu, Tao, Ye & Lu, 2016). This can be synethesized byresearch
done by Mao, Fidan & Anderson 2007) and this has contributed extensively on this aspect. The
body of literature will help provide comprehensive literature to the research gap.
2.4.3 Magnetic Field
The research has been done extensively by Yuntian, Tao & Andong (2006), on integrating
magnetic Field for the fingerprinting based on the indoor positioning system in this subject. The
authors have provide comprehensive research with this subject by emphasizing that magnetic
field data as the fingerprints to the Smartphone the indoor positioning could become popular to
the modern times ( Mao , Fidan & Anderson , 2007 ) . The filters could be utilized to develop on
the accuracy. Moreover, they found out that existing particle filters depending on the methods
might heavily be afflicted with the motion estimation errors which might result to the unreliable
system which impose strong restrictions to the Smartphone. Bolat & Akcakoca ( 2017 ) research
on hybrid indoor positioning depending on Magnetic field have also highlight that it could be
used to create a fingerprint maps by using the field sensors (Paul & Sato, 2017). However, the
technique has some drawback since a particular location could have similar distortions which are
far away from the current location.
2.4.4 Hybrid systems
On the related literature in the hybrid system is the work done by Bolat & Akcakoca (2017)
discusses how the hybrid system arises from the embedded control when the digital controllers,
and the subsystems which is modeled as the finite state machine. The authors have combined
numerous methods to the localization algorithms that are dependent on the hop count depending
on the data between anchor nodes and the sensors (Liono, Qin & Salim, 2016). There is also
work done by Li et. al (2012) on sensor stream in multi- activity recognition discusses the aspect
of hybrid system in reliability point of view, but this research does not examine how it enhances
on the accuracy to identity the distance between intermediate nodes.
2.5 Sensors Fusion
There are numerous literatures on the sensor fusion; one related work is that of Röbesaat, Zhang,
Abdelaal & Theel (2017) on improving indoor localization. The authors have highlighted that to
improve on the accuracy to pose estimation, there are numerous sensors which are available to
the mobile robots which could measure variables which are associated to the motion are used to
localize on the robots (Röbesaat, Zhang, Abdelaal & Theel, 2017). The different measurement
are usually combined in with an algorithm in order to take into the account various accuracy and
level of the noise to each sensor. Varshney, Goel & Qadeer, (2016) research proposed that the
most employed fusion approach is Kalman Filter or one of the variants for the nonlinear system.
The authors have also highlighted that there are several illustration of KF that depend on the
localization algorithms developed to different variation of mobile robots and sensors.
Nonetheless, these literatures lack information on how to use the complex fusion schemes to the

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mobile robots and how it can enhance on this technology (Varshney, Goel & Qadeer, (2016).
According to King, Kopf & Effelsberg (2005), they recently published on the location system
add as the third part to the sensor fusion which could combine on the position estimates which
are obtained from the numerous locations determination algorithms as well as different sensors.
This idea has seen results which are generated by the sensor fusion algorithm to be more precise
than the position estimate which are provided one kind of the sensor (Khoshelham & Zlatanova,
2016). This has been an improvement to the past research particular where there was position
estimate where there was use of one sensor (Khoshelham & Zlatanova, 2016). In this research
literature, it would help highlights how sensor fusion has been a promising technique in solving
the aspect of the position estimate conflict in which the authors in that research did not address.

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References
Chen, K., & Vadde, K. R. (2012). Design and evaluation of an indoor positioning system
framework. UC Berkeley course project for CS262A.
Galván-Tejada, C. E., Carrasco-Jimenez, J. C., & Brena, R. (2013). Location identification using
a magnetic-field-based FFT signature. Procedia Computer Science, 19, 533-539.
Jiaxing, L. (2017). The Design and Implementation of Indoor Localization System Using
Magnetic Field Based on Smartphone. International Archives of the Photogrammetry,
Remote Sensing & Spatial Information Sciences, 42.
Khoshelham, K., & Zlatanova, S. (2016). Sensors for indoor mapping and navigation.
King, T., Kopf, S., & Effelsberg, W. (2005). A Location System based on Sensor Fusion:
Research Areas and Software Architecture. Informatik-Berichte, 324, 28-32.
Kubrak, D., Le Gland, F., He, L., & Oster, Y. (2009, September). Multi―sensor fusion for
localization. Concept and simulation results. In Proceedings of the 2009 ION Conference
on Global Navigation Satellite Systems, Savannah 2009 (pp. 767-777).
Le Grand, E., & Thrun, S. (2012, September). 3-axis magnetic field mapping and fusion for
indoor localization. In Multisensor Fusion and Integration for Intelligent Systems (MFI),
2012 IEEE Conference on (pp. 358-364). IEEE.
Li, F., Zhao, C., Ding, G., Gong, J., Liu, C., & Zhao, F. (2012, September). A reliable and
accurate indoor localization method using phone inertial sensors. In Proceedings of the 2012
ACM Conference on Ubiquitous Computing (pp. 421-430). ACM.
Liono, J., Qin, A. K., & Salim, F. D. (2016, November). Optimal time window for temporal
segmentation of sensor streams in multi-activity recognition. In Proceedings of the 13th
International Conference on Mobile and Ubiquitous Systems: Computing, Networking
and Services (pp. 10-19). ACM.
Mao, G., Fidan, B., & Anderson, B. D. (2007). Wireless sensor network localization techniques.
Computer networks, 51(10), 2529-2553.
Paul, A. K., & Sato, T. (2017). Localization in Wireless Sensor Networks: A Survey on
Algorithms, Measurement Techniques, Applications and Challenges. Journal of Sensor
and Actuator Networks, 6(4), 24.
Röbesaat, J., Zhang, P., Abdelaal, M., & Theel, O. (2017). An Improved BLE Indoor
Localization with Kalman-Based Fusion: An Experimental Study. Sensors, 17(5), 951.
Varshney, V., Goel, R. K., & Qadeer, M. A. (2016, July). Indoor positioning system using Wi-Fi
& Bluetooth Low Energy technology. In Wireless and Optical Communications Networks
(WOCN), 2016 Thirteenth International Conference on (pp. 1-6). IEEE.

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Wang, R., Zhao, F., Luo, H., Lu, B. and Lu, T., (2011), September. Fusion of wi-fi and bluetooth
for indoor localization. In Proceedings of the 1st international workshop on Mobile
location-based service (pp. 63-66). ACM.
Xiao, W., Ni, W., & Toh, Y. K. (2011, September). Integrated Wi-Fi fingerprinting and inertial
sensing for indoor positioning. In Indoor Positioning and Indoor Navigation (IPIN), 2011
International Conference on (pp. 1-6). IEEE.
Xie, H., Gu, T., Tao, X., Ye, H., & Lu, J. (2016). A reliability-augmented particle filter for
magnetic fingerprinting based indoor localization on smartphone. IEEE Transactions on
Mobile Computing, 15(8), 1877-1892.