Enhancing indoor localization using IoT techniques Master Thesis - Defence Seminar

1. By
Mohamed Nabil
Under Supervision
Prof. Mohamed Bakr Abdelhalim
Dr. Ashraf AbdelRaouf
Enhancing indoor localization
using IoT techniques

2. Agenda
Introduction
Motivation
Problem Statement
Related Work
Kalman Filter
Internet of Things (IoT)
Proposed Framework
Experiment
Results
Future work

3. Introduction
Smartphones contains a GPS unit, allow applications such as geo-fencing and
automotive navigation.
Lack of GPS signal reception in indoor environments.
Inertial Pedestrian Dead-Reckoning (PDR) used for indoor navigation on
smartphones.
Introducing an implementation for PDR algorithm using Quaternion-Based
Extended Kalman filter “EKF” for heading estimation with Low Pass filter and
adaptive step length methodology.
Our approach shows a remarkable decreases of the error, the recorded average
error is 0.16 meter with percentage 0.07% of the 210 meter total traveled.

4. Introduction - Positioning
Determine the position of an object.
Four different categories of positioning methods are typically used
for positioning systems.
◦ Radio-based Positioning Systems (GSM).
◦ Global Positioning System (GPS) (Most accurate).
◦ Wide-area / Local-area Systems (Wi-Fi).
◦ Near-field Systems (BLE).
These are based on the cell of origin, distances, angles, or pattern
recognition.

5. Introduction – Indoor Positioning
Techniques
Location Fingerprinting
◦ Record the occurrence of different
radio- signals in a specific location,
and measure the signal-strengths
using a mobile device, as physical
locations in the world have unique
radio signatures.
Triangulation/Trilateration
◦ Lengths between the wireless
device and each of the Access
Points (APs) the device is
connected to.
Proximity
◦ Simplest techniques, where the
location of a wireless device is
estimated to be the same as the
location of the AP it is currently
connected to.

6. Introduction – Other Indoor Positioning
Systems
Dead reckoning (DR) is the process of estimating the current
position of a user based upon a previously known position.
DR systems estimate relative displacement by step detection
and user heading estimation.
Used in Marine, Air, Automotive and Autonomous navigation
in robotics.
Pedestrian dead reckoning (PDR) using smartphone built-in
accelerometers can be used as a pedometer and built-in
magnetometer as a compass heading provider.

7. Kalman Filter
Kalman filter exists for the past 50 years.
It was first introduced by Rudolf Emil Kalman in 1960
and was implemented on the Apollo Project in 1961.
It's a method of predicting the future state of a
system based on the previous ones.
Consists of two distinct processes, the prediction
process and the measurement process.
Operated in a recursive manner to achieve optimal
Kalman filtering process.

8. Quaternion-based Extended Kalman
Filter
A complex number of the form 𝑤 + 𝑥𝑖 + 𝑦𝑗 + 𝑧𝑘, where w, x, y, z are real
numbers and i, j, k are imaginary units that satisfy certain conditions.
Quaternion-based EKF is used for determining orientation using 9-degree
of freedom “DOF”:
◦ 3-axis angular velocity
◦ 3-axis acceleration
◦ 3-axis magnetic field sensors.
10 states :
◦ 4-D quaternions.
◦ 3-D acceleration bias.
◦ 3-D magnetic field bias were modeled.

9. Quaternion-based EKF – Cont.
θ, ϕ, ψ represent pitch, roll and yaw angle respectively according to the Euler
angles definition.
Quaternion q is defined by:
𝑞 = 𝑞0 𝑞1 𝑞2 𝑞3 = cos
𝛼
2
𝑟𝑥 sin
𝛼
2
𝑟𝑦 sin
𝛼
2
𝑟𝑧 sin
𝛼
2
(1)
A 3-D vector can be rotated by a quaternion 𝑞 using the following equation:
𝜐 𝑛 = 𝑞 ⊕ 𝜐 𝑏 ⊕ 𝑞∗ (2)
where 𝜐 𝑛 and 𝜐 𝑏 are described in n frame and b frame vectors
𝑞∗ is the conjugate of 𝑞 and is given by:
𝑞∗
= 𝑞0 − 𝑞1 − 𝑞2 − 𝑞3 (3)

10. Quaternion-based EKF – Cont.
Rather than a complex rotation equation described by Equation (2), a simpler rotation
relationship can be expressed as the Direction Cosine Matrix (DCM) in terms of the quaternion
𝑞:
𝐶 𝑏
𝑛
=
𝑞0
2
+ 𝑞1
2
− 𝑞2
2
− 𝑞3
2
2 𝑞1 𝑞2 − 𝑞0 𝑞3 2 𝑞1 𝑞3 − 𝑞0 𝑞2
2 𝑞1 𝑞2 − 𝑞0 𝑞3 𝑞0
2
− 𝑞1
2
+ 𝑞2
2
− 𝑞3
2
2 𝑞2 𝑞3 − 𝑞0 𝑞1
2 𝑞1 𝑞3 − 𝑞0 𝑞2 2 𝑞2 𝑞3 − 𝑞0 𝑞1 𝑞0
2
− 𝑞1
2
− 𝑞2
2
+ 𝑞3
2
(4)
According to the Z-Y-X aerospace sequence, Euler angles 𝜃, ϕ and ψ can be written as the
following identity:
𝜃
∅
𝜓
=
𝑎𝑡𝑎𝑛2 2𝑞2 𝑞3 + 2𝑞0 𝑞1, 𝑞3
2
− 𝑞2
2
− 𝑞1
2
− 𝑞0
2
−asin (2𝑞2 𝑞3 − 2𝑞0 𝑞2)
𝑎𝑡𝑎𝑛2 2𝑞1 𝑞2 + 2𝑞0 𝑞3, 𝑞1
2
− 𝑞0
2
− 𝑞3
2
− 𝑞2
2
(5)

11. Quaternion-based EKF – Cont.
The relationship between the quaternion derivative 𝑞 and the angular velocity 𝜔
are described by the following well known equation:
𝑞 =
1
2
× 𝑞 ⊗ 𝜔 (6)
where ⊗ represents quaternion multiplication.

12. Internet of Things (IoT)
Things are objects of the physical world or
of the information world (virtual).
Things are capable of being identified and
integrated into communication layer.
Physical things: surrounding environment,
sensors, electrical equipment, etc.
Virtual things are capable of being stored,
processed and accessed: multimedia
content.

13. Internet of Things (IoT) – Cont.
Definition : A global infrastructure for the information society,
enabling advanced services, by interconnecting (physical and virtual)
things based on existing and evolving interoperable information and
communication technologies.
The basic concept of IoT is to connect things together, thus enabling
these “things” to communicate with each other and enabling people
to communicate with them.

14. IoT Fundamental characteristics
Interconnectivity
◦ IoT devices are integrated into the information network.
◦ can be dynamically discovered in the network
◦ Have the capability to describe themselves
Heterogeneity
◦ based on different hardware and networks
◦ They have to interact with other devices through different networks
Dynamic changes & self adapting
◦ Take actions based on operating conditions.
◦ Sleeping/waking up
◦ Connected/disconnected
Enormous scale
◦ IoT devices are much bigger than the number of devices on Internet
◦ big data
◦ semantics of data
◦ data handling (Cloud?)

15. IoT Applications
Smart cities: more digitalized and intelligent cities.
Smart factory: IoT will provide automatic procedures.
Smart home.
Wearables.
Smart grids.
Connected car.
Personal health devices.
Smart retail.
Smart supply chain.
Smart farming.

16. Motivation
The market of indoor positioning applications will
worth $4.4 Billion by 2019.
More than 5.6 billion IoT devices in Q3 2016, and the
number is expected to increase to 18.1 billion by 2022.
Cisco estimates that the Internet of Things has a
potential value of $19 trillion over the next decade.
Wearable tech market to be worth $34 Billion by 2020.
People sometimes loose directions inside unfamiliar
and large buildings.
A quaternion-based extended Kalman filter (EKF)
algorithm has been shows a massive improve in
heading estimation with handheld IMUs in experiment
and theory.

17. Motivation – Cont.
Current systems rely on a wireless network which is not always available and
some buildings topologies are hard to get covers and very expensive ($349 /
50 square meter ).
Indoor Location Systems is tremendously necessary for many fields:
• The Military.
• Emergency Services (e.g. earthquakes, firefighting).
• Disaster Rescue.
• Tracking Of Doctors In Hospital.
• Tourism.
• Navigate passengers in airports.
• Help visually impaired people to navigate.
In December 2015, British government building a project to probe the
viability of using low power Bluetooth Beacon technology for indoor
navigation.

18. Problem Statement
Locating user or object in an indoor environment is a challenge task since the lack of GPS signal.
Smart phones and wearables devices today come with a vast types of sensors which can be used and
combined to locate the user in indoor environment.
Massive number of IoT device which we can use to enhance the accuracy for locating human in
indoor environment.
Reading of these sensors can be affected by a number of errors.
The output of sensors readings must go through some filtering algorithm.
The filtered output can be used in the user location estimation, to achieve this by applying a type of
Dead Reckoning Algorithms.

19. Problem Statement – Cont.
The firefighters are equipped with small
lightweight sensors that do not interfere
with their ability to do their job.
The positioning system works in real
time, positioning each user with meter
level accuracy and broadcasting the
information.
The information is presented to the
operational manager overlaid on an
informative map.

20. Related Work
Three groups of Indoor Positioning Systems (IPS) have been
introduced.
1. Fingerprint: signature of environment features and strongly dependent on
the physical location.
2. Beacons : installing infrastructures beacons in a building.
3. Smartphones and wearables with inertial sensors built-in: that make it
possible to deploy PDR systems in daily life tasks.

21. Related Work Cont.
Year / Author Title Description Accuracy Challenges
2010/ Wei Chen, Ruizhi Chen,
Yuwei Chen, Heidi Kuusniemi,
Jianyu Wang
An Effective Pedestrian Dead
Reckoning Algorithm Using a
Unified Heading Error Model
Integrate GPS with self-
contained dead rocking sensors
to overcome the GPS signal
corruption or unavailability.
Below than 1.5% of the traveled
distance.
• Solution not compatible with
mobile phones.
• Highly dependent on GPS as
an input sensor.
2011/ Kamil Kloch, Paul
Lukowicz, Carl Fischer
Collaborative PDR Localization
with Mobile Phones
Ad-hoc collaboration between
devices to improve the PDR
algorithm.
Improve the indoor position in
78% of the cases
• It is mainly used to cover large
events.
• Depends on the collaboration
between devices, so all
devices in the same closed
area must have the same
system.
• Use GPS in the calculations.

22. Related Work Cont.
Year / Author Title Description Accuracy Challenges
Jochen Seitz, Jasper Jahn, Javier
Guti_errez Boronat, Thorsten
Vaupel, Steffen Meyer, Jorn
Thielecke
A Hidden Markov Model for
Urban Navigation Based on
Fingerprinting and Pedestrian
Dead Reckoning
Using PDR algorithm with WiFi
fingerprint to overcome the
unviability or WiFi outage.
Achieve high accuracy by
combining WiFi fingerprint with
Hidden Markov Model(HMM)
• Depend on Wi-Fi fingerprint
2012/ Azkario Rizky Pratama,
Widyawan, Risanuri Hidayat
Smartphone-based Pedestrian
Dead Reckoning as an Indoor
Positioning System
Using the PDR algorithm to
recognize the pattern of human
steps and apply high/low pass
filters
Average error of 2.925% or 1.39
meter of the traveled distance
• It is difficult to detect
displacement.
• Not using any algorithm to
enhance the results.
2015/ Chengxuan Liu, Ling Pei,
Jiuchao Qian, Lin Wang, Peilin Liu,
Wenxian Yu
Sequence-Based Motion
Recognition Assisted Pedestrian
Dead Reckoning Using a
Smartphone
Using a sequence-based motion
recognition method which
estimates the motion states from
a sequence of data, and deploys a
HMM (Hidden Markov Model) to
infer the state labels of sequence
motion.
0.30 m Mean positioning error. • Solution not compatible with
mobile phones.

23. Related Work Cont.
Year / Author Title Description Accuracy Challenges
2011/Andre M. Cavalcante,
Edgar B. Souza, Juliano J.
Bazzo, Nibia Bezerra, Allan
Pontes and Robson D. Vieira
A Pedometer-Based System
for Real-time Indoor Tracking
on Mobile Devices
Using a simple pedometer
approach that uses data
collected from sensors
(accelerometers and
compass) that are built-in on
mobile devices to provide
indoor tracking.
Low to moderate accuracy  Low accuracy due to
using low cost sensors
and mobiles (N97)
which presenting low
tolerance to noise and
electromagnetic
interference.
 Did not use any filtering
and interference
reduction strategies.

24. Related Work Cont.
Year / Author Title Description Accuracy Challenges
2010/Dominik Gusenbauer,
Carsten Isert and Jens
Krösche
Self-Contained Indoor
Positioning on Off-The-Shelf
Mobile Devices.
Using a combination of GPS,
where available, with
Pedestrian Dead Reckoning
(PDR) utilizing inertial
measurements and context-
aware activity based map
matching.
Approximately a single
parking spot (5 M).
 Low accuracy due to
using of low cost
sensors and mobiles
(N97) which present low
tolerance to noise and
electromagnetic
interference.
 Did not use any filtering
and interference
reduction strategies.
 Used GPS as input
method to increase the
accuracy which is not
available all the time.

25. Related Work Cont.
Year / Author Title Description Accuracy Challenges
2010/ Masakatsu Kourogi,
Tomoya Ishikawa, and Takeshi
Kurata
A Method of Pedestrian Dead
Reckoning Using Action
Recognition
Using a single low-cost Inertial
Measurement Unit (IMU)
mounted at the waist of the
user and using an action
recognition process to detect
the current action taken by the
user such as standing up
from/sitting down on a chair,
and bending over to slip
through obstacles.
Achieved 95% by cross
validation test on the training
data set, and error rate of the
PDR localization is 2% of the
walking distance.
 Solution not compatible
with mobile phones.
 Depends on many sensors
to collect data
(Accelerometers,
Gyroscope, Magnetometers,
Thermometers, Barometer)
which not all available in
mobile phones.
 They achieve results by
assuming that the specified
actions (going up stairs,
going down stairs, take the
elevator … etc.) are likely to
take place at the fixed
positions.

26. Related Work Cont.
Year / Author Title Description Accuracy Challenges
2015/ Qinglin Tian, Zoran
Salcic, Kevin I-Kai Wang, Yun
Pan
An Enhanced Pedestrian Dead
Reckoning Approach for
Pedestrian Tracking using
Smartphones
Building a pedestrian tracking
using dead reckoning on a
standard smartphone.
Assuming the device is carried
in a defined way within the
tracking period, by identifying
three typical modes of
carrying the device during
walking and using that fact to
enhance tracking accuracy.
4.5% of the total traveled
distance.
 Not using any map
matching techniques.
 A very low accuracy.

27. Related Work – Cont.
A. R. Pratama, proposed a system with error rate of 2.925% for a 30 meters long
experiment which is about 0.6 meter error.
Y. Jin, proposed a system with error rate of 26.3% for a 90 meter long
experiment.
T. Qinglin, proposed a new approach of PDR with an error rate of 4.69% of the
total travel distance of 96.33 meter.

28. Proposed
Framework

29. Proposed Framework Cont.
We used the quaternion-based EKF model.
Low Pass Filter “LPF” and mean filter used to smooth sensors
readings.
Inputs of the algorithm are accelerometer, magnetometer and
gyroscope readings.
Output is the current user heading in radians.
Mean filter is applied on magnetometer and gyroscope readings.
LPF is only applied on accelerometer readings.

30. Step-based Pedestrian Dead-reckoning
According to the PDR algorithm, the accelerometer, the magnetic sensor and the
gyroscope will be combined to locate the position of the smartphone based on
the following formula:
𝑋𝑖+1 = 𝑋𝑖 + 𝑆𝐿𝑖 × sin 𝛼𝑖
𝑌𝑖+1 = 𝑌𝑖 + 𝑆𝐿𝑖 × cos 𝛼𝑖
(7)
where (𝑋,𝑌) indicates the coordinates of the position, 𝑆𝐿𝑖is the step length and 𝛼
represents the heading angle

31. Step Detection
To detect a step, we employ a relative
threshold detection scheme .
This scheme detects a step when valid
maximum peak (as maxima) and valid
minimum peak (as minima) are detected in
sequence of a certain interval.
To ensure a valid step, an interval time
difference between maxima and minima is
also determined experimentally, must be
between 400ms – 1000ms.
9.2
9.4
9.6
9.8
10
10.2
10.4
10.6
41 42 43 44 45 46 47 48 49
PROJECTEDVERTICALACCELERATION(M/S2)
TIME (S)

32. Orientation Estimation
We used 6-D vectors (3-axis acceleration and 3-axis magnetic field), Quaternion-
Based EKF.
We used 7-D (4-D quaternions and 3-D gyroscope bias drift) vectors as state vectors.
In practice, the gyroscope bias drift specified by temperature is relatively small.
Accelerometer and magnetometer are usually utilized to eliminate the gyroscope
bias drift.
Therefore, 4-D state vectors are:
𝑥1
𝑥2
𝑥3
𝑥4
=
𝑞0
𝑞1
𝑞2
𝑞3
(8)

33. Orientation Estimation – Cont.
According to equation 6 , state equations can be expressed as:
𝑥1
𝑥2
𝑥3
𝑥4
=
1
2
𝑥1
𝑥2
𝑥3
𝑥4
⨂
0
𝜔 𝑥
𝜔 𝑦
𝜔𝑧
(9)
6-D measurement vectors can be directly obtained by the accelerometer and magnetometer
output:
𝑧1
𝑧2
𝑧3
𝑧4
𝑧5
𝑧6
=
𝑎 𝑥
𝑎 𝑦
𝑎 𝑧
𝑚 𝑥
𝑚 𝑦
𝑚 𝑧
(10)

34. Step Length Estimation
Static and dynamic technique.
Static technique assumes that any valid step that has the same length, which can be specified through the
following equation:
𝑠𝑡𝑒𝑝_𝑠𝑖𝑧𝑒 = ℎ𝑖𝑔ℎ𝑡 × 𝑘 (11)
with k equal to 0.415 for male and 0.413 for female.
We use the dynamic technique to estimate the step length ρk from acceleration measurements for the kth
step as:
𝜌 𝑘 = 𝐾 ×
4
𝑎 𝑘
𝑣−𝑚𝑎𝑥
− 𝑎 𝑘
𝑣−𝑚𝑖𝑛
(12)
where 𝑎 𝑘
𝑣−𝑚𝑎𝑥
and 𝑎 𝑘
𝑣−𝑚𝑖𝑛
are the maximum and minimum values of the projected vertical acceleration
during the kth step, respectively. The constant K is dependent on each pedestrian.
K=0.45 experimentally

35. Floor detection
Using:
𝐻 = −
𝑅.𝑇
𝑀 𝑎.𝑔
ln
𝑝 𝐻
𝑝 0
◦ where 𝑝 0 is pressure at Mean Sea Level (MSL), 𝑀 𝑎 is the molecular
weight of air, 𝑔 is the gravitational constant, 𝑅 is the universal gas
constant, and 𝑇 is the temperature in Kelvin
to determine the current user height
related to the sea level.
The first height reading is the first floor.
Every floor will have a fixed height of 2.7 to
3.25 meters.

36. Experiment Methods
compute the error rate by calculating the difference between the start and end point which is
GPS coordinates by Haversine formula as following:
𝑎 = sin
∆𝜑
2
2
+ cos 𝜑1 ∗ cos 𝜑2 ∗ sin
∆𝜆
2
2
𝑐 = 2 ∗ 𝑎 tan 𝑎, 1 − 𝑎
2
𝑑 = 𝑅 ∗ 𝑐
where 𝜑 is latitude, 𝜆 is longitude, R is earth’s radius (mean radius = 6,371km)

37. Experiment Equipment
One plus one
◦ LIS3DH Accelerometer, AK8963 Magnetometer
and L3GD20 Gyroscope.
◦ The device used in this experiment has Android
6.0.
◦ Compatible with any Android phone.
◦ with Accelerometer, Magnetometer and
Gyroscope sensors.
Samsung Gear S2
◦ Accelerometer, gyro, heart rate, barometer
sensors. It is running Tizen OS 2.4 for wearable.

38. Experiment Setup
Start point and end point with a
180°difference between them.
Total test distance is 10 meters.
Test subjects take varies number of steps,
between 15 to 20 steps.
The axis of the acceleration have a 90-degree
difference between the smartphone and the
gear S2.
Repeated two times, first without wearing a
smart watch and second try with a smart
watch.

39. Results : Orientation Estimation
Gyroscope and Magnetometer are fused together to provide a better estimation.
Applied Kalman filter, mean filter and low pass filter to smoothing the readings and remove any
noises
-1
-0.5
0
0.5
1
1.5
2
2.5
0
7
14
21
28
35
42
49
56
63
70
77
84
91
98
105
112
119
126
133
140
147
154
161
168
175
182
189
196
203
Heading Axis Without Filters
Azimuth Pitch Roll
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
0
7
14
21
28
35
42
49
56
63
70
77
84
91
98
105
112
119
126
133
140
147
154
161
168
175
182
189
196
203
Heading Axis With Filters
Azimuth Pitch Roll

40. Results : Step Detection and Length
Estimation
To evaluate the step detection and it is length a distance of approximately 10 meters was walked
21 times.
Step detection Mean error 2.1 step with variance of 1.66 step.
Step length mean error 0.168 meter with variance 0.002203 meter.
0
5
10
15
20
25
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
67
70
73
76
79
82
85
88
91
94
97
100
Actual Vs. Detected Steps
Actual steps Detected steps
0
0.2
0.4
0.6
0.8
1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Actual Vs. Recorded Stesp Length
actual steps length recorded steps length

41. RESULTS : Watch to Smartphone
connection
Using HTML5 WebSocket (peer to peer) since the Samsung Gear S2
have a full HTML5 API implementation.
data structure as the following:
◦ < accelerometer data>; <gyroscope data>; <barometer data>; <time
stamp>
A sample of data sent from the watch to smartphone:
◦ -0.8757730722427368;6.183053970336914;7.331608772277832;-
1.8200000524520874;1.190000057220459;0.9800000190734863;1012.
55;1477159557796
Compared to the Samsung SDK to communicate the Gear S2 with
Smartphone :
◦ Samsung rate is 24 readings per second
◦ The proposed solution is 41 readings per second

42. Pedestrian Tracking Results – First
Scenario
7 subjects each one repeated the test 3 times,
different in gender and height.
Total travel distance per subject is 30 meter.
Total distance traveled by all subjects is 210
meters.
Without wearing the smartwatch.
The average error is 0.3 meter with percentage
0.14% of the total traveled distance.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
AverageErrorinMeter
Subject
Average error over all test subjects

43. Pedestrian Tracking Results – Second
Scenario
Same test conditions on the same subjects.
Wearing the smartwatch.
The average error is 0.16 meter with percentage
0.07% of the total traveled distance.
An improvement of 46% by wearing the
smartwatch.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
AverageErrorinMeter
Subject
Average error over all test subjects

44. EXPERIMENT AND RESULTS
Compared to the other techniques, our proposed algorithm results
an improve by :
◦ 40.16% compared to Y. Jin.
◦ 30.6% compared to A. R. Pratam.
◦ 14.2% compared to T. Qinglin.
Shows that our technique is a promising one.

45. FUTURE WORK
Test the proposed algorithm in much more
wearable devices:
◦ Samsung Gear S3 which include a GPS sensor by
default, this will helps in the heading estimation.
◦ Fitbit surge which includes GPS, 3-axis
accelerometers, 3-axis gyroscope, digital compass
and altimeter.
◦ GARMIN fēnix® 5 which include GPS, GLONASS,
Barometric altimeter, Compass, Gyroscope,
Accelerometer and Thermometer sensors.

46. FUTURE WORK – Cont.
Combining the proposed algorithm with other indoor navigation techniques :
◦ Wi-Fi RSSI fingerprinting
◦ ZigBee
◦ RFID
◦ Camera technique which is very promising (Google Visual Positioning
Service “VPS”)
Testing the proposed algorithm on the autonomous drone to facilitate and
add the capability for indoor navigation to theses drones.
Testing the proposed algorithm on self-driving cars to allow these cars
navigate in indoor environments when the there is no GPS signal.
Tuning of algorithm parameters.
Sensitivity analysis.
More experiments.

47. Published Papers
Title A new Kalman filter-based algorithm to
improve the indoor positioning
Authors Mohamed Nabil, M. B. Abdelhalim, Ashraf
AbdelRaouf
Publisher IEEE
Conference Multimedia Computing and Systems (ICMCS),
2016 5th International Conference on
Date of
Conference
29 Sept - 1 Oct 2016
Conference
Location
Marrakech, Morocco
DOI 10.1109/ICMCS.2016.7905588
Link http://ieeexplore.ieee.org/document/7905588/
Title Enhancing indoor localization using IoT techniques
(Submitted)
Authors Mohamed Nabil, M. B. Abdelhalim, Ashraf
AbdelRaouf
Publisher Springer
Conference The 3rd International conference on Advanced
Intelligent systems and Informatics 2017
Date of
Conference
15 Sept. 2017
Conference
Location
Cairo, Egypt

48. Thank You