This document presents research on improving tracking accuracy for indoor situational awareness systems through dynamic movement of reference nodes. It proposes using directional antennas on mobile targets and placing reference nodes outdoors to maintain good geometry for triangulating target positions. The research aims to develop a positioning, location, and tracking algorithm based on this approach and demonstrate its performance compared to previous work. Future work plans to extend the algorithm to 3D and add node authentication. Results show the dynamic node method achieves errors as low as 0.24% without noise, increasing when noise is introduced.

1. Electrical and Computer Engineering Department
Roy G. Perry College of Engineering, Prairie View A&M University,
Prairie View, TX
By
Dr. Warsame H. Ali
DYNAMIC MOVEMENT OF REFERENCE NODES,
FOR IMPROVED TRACKING ACCURACY OF
INDOOR SITUATIONAL AWARENESS SYSTEMS
DYNAMIC MOVEMENT OF REFERENCE NODES,
FOR IMPROVED TRACKING ACCURACY OF
INDOOR SITUATIONAL AWARENESS SYSTEMS
07/22/15 ICGST 2012 Presented July 17

2. OUTLINEOUTLINE
 Problem Statement
 Overview
 Scope
 Background
 Proposed research
 Result Analysis
 Conclusion
 Future work
07/22/15 ICGST 2012 Presented July 17

3. PROBLEM STATEMENTPROBLEM STATEMENT
• Design a Position, Location and Tracking(PL&T) algorithm based on
dynamic references for Indoor Situational Awareness System
07/22/15 ICGST 2012 Presented July 17

4. 07/22/15 ICGST 2012 Presented July 17
• GPS have been traditionally used for Position, Location and Tracking
(PL&T) but In several cases, GPS tends to be inaccurate in indoor
situations.
• It also tends to be inaccurate near buildings(accuracies of 20 meters)
• A self-configuring network, Mobile Ad hoc Network (MANET) is used
for robust prediction and tracking in indoor situational awareness
systems (ISM).
• Researchers have looked into the implementation of MANET using
various schemes. A common scheme is triangulation.

5. 07/22/15 ICGST 2012 Presented July 17
• Indoor PL&T algorithm using dynamic references

6.  This project is a follow up on two previous work done in Prairie View
A&M University.
The senior design team at Prairie View A&M University was
saddled with the task of designing a PL&T algorithm. They came
up with the “Panther Tracker”, that uses autonomous time and
speed movement to estimate the range of a robot from its previous
position
A dissertation by “Niraj “on Antennas, Dynamic PL&T and the
security of the system
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7. • In estimating the distance of a node
from its neighbor, the node can use the
time of flight, attenuation of signal
strength or directionality of a signal
emanating from its neighbor.
LOCATION ESTIMATION TECHNIQUESLOCATION ESTIMATION TECHNIQUES
• Traditional methods of Estimation are
triangulation, scene analyses and proximity
• Triangulation remains the most commonly used
method with the other methods used mainly to
improve its accuracy.
• Triangulation relies on the geometric properties
of triangles to estimate the location of a target
node
• Lateration triangulation-distance measurements
• Angulation triangulation-angle measurements in
combination
R2
R1
R3
θ
d
β
MS
BS BS
Lateration
Angulation
07/22/15 ICGST 2012 Presented July 17

8. • Difficult to implement
• Due to accuracy limitations
stemming from lack of stable clock
and random delays from contention
method in IEEE 802.11 wireless
LAN, this design method was
stopped for demonstration.
TOD/TOA APPROACHTOD/TOA APPROACH
• Di is computed using TOD and
TOA roundtrip measurement
• For each triangle, two ranges are
computed for each Di and take the
average to get D1, D2, D3.
• With known Di and reference (x, y),
• The coordinate of the target(T) can
be calculated using each triangle.
• For close values of (x, y), take the
average of the three (x, y)s
• For two close value and one off-
valued result, take the average of
the close results
• For three off-value results, repeat
measurement.
07/22/15 ICGST 2012 Presented July 17

9. DIRECTIONAL ANTENNADIRECTIONAL ANTENNA
• Realized by using the beamforming
technology together with an antenna
array
• Transmit beamforming-when
implemented at Tx only
• Receive beamforming-when
implemented at Rx only
• Joint transmit and receive
beamforming-when implemented at Tx
& Rx
θ
z
x
y
07/22/15 ICGST 2012 Presented July 17

10. • Directional antennas have a longer transmission range
• reducing delay and increase in connection throughput
• Directional antenna also possess larger free channel space
around the transmitting node and allows more concurrent
transmission without collisions
• A two element directional antenna was found to consume
approximately 7% of the power of the omnidirectional
antenna while a four-element antenna comes just 1%.
• Used in Cellular networks such as GSM and infrastructure
WLAN
WHY USE DIRECTIONAL
ANTENNA ?
WHY USE DIRECTIONAL
ANTENNA ?
07/22/15 ICGST 2012 Presented July 17

11. MULTIPATH FADED CHANNELMULTIPATH FADED CHANNEL
• Occur when plane waves arriving at the
MS/BS from the MS/BSs with random
phases and combine vectorially at the
receiver
• Multipath-fading results in rapid
variations in the envelope of the
received signal at the MS/BS
Multipath causes difference in propagation
lengths which results in amplitude and
phase fluctuations and time delay in the
received signal
 Rayleigh fading occurs when the
multiple reflective paths are large in
number and there is no LOS signal
component
 When Multipath-fading has a dominant
LOS, it can be modeled by the Rician
distribution
 the envelope of the received
signal is described statistically as
Rayleigh distribution.
07/22/15 ICGST 2012 Presented July 17

12. 07/22/15 ICGST 2012 Presented July 17
•Allow mobile Targets equipped with directional antennas to move
freely indoors
•Dynamic references are placed outdoors in order to maintain good
geometry(≥30 angle at each vertex of the triangle) for tracking the⁰
targets.
•Determine the ranges between references and each target.
•Translate ranges into (X,Y) Coordinates of a target.
•Demonstrate the performance analytically and compare with the
senior design implementation.

13. INDOOR TRIANGULATION
ARCHITECTURE
INDOOR TRIANGULATION
ARCHITECTURE
B1
A
B2
T1
T2
07/22/15 ICGST 2012 Presented July 17

14. FLOW MODEL FOR DYNAMIC
MOVEMENT OF REFERENCES
FLOW MODEL FOR DYNAMIC
MOVEMENT OF REFERENCES
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15. α
µ
θ
β
DYNAMIC NODES WITH DIRECTED BEAMDYNAMIC NODES WITH DIRECTED BEAM
07/22/15 ICGST 2012 Presented July 17

16. LATERATION WITH MOBILE NODESLATERATION WITH MOBILE NODES
The system is then solved using the least square approch
Lateration equation
07/22/15 ICGST 2012 Presented July 17

17. Runs Real Real Calculated Calculated Real DT-N Calculated Change in
X Y X Y A DT-N DT-N Abs(Error) %Error
1 7.3078 9.9286 7.2223 9.9406 12.3281 12.2873 0.0408 0.0408 0.3308
2 7.3078 9.9286 7.3720 10.1467 12.3281 12.5420 -0.2139 0.2139 1.7354
3 5.0000 8.0000 4.9577 7.9339 9.4340 9.3555 0.0785 0.0785 0.8318
4 5.0000 5.5000 4.6028 5.1119 7.4330 6.8788 0.5543 0.5543 7.4570
5 10.0000 8.0000 9.4708 7.6693 12.8062 12.1866 0.6196 0.6196 4.8383
6 10.0000 8.0000 9.8985 8.0156 12.8062 12.7370 0.0693 0.0693 0.5411
7 14.0000 8.5000 14.0057 8.4154 16.3783 16.3395 0.0389 0.0389 0.2373
8 14.0000 8.5000 13.8415 8.3168 16.3783 16.1479 0.2304 0.2304 1.4067
9 14.0000 8.5000 13.9886 8.4052 16.3783 16.3196 0.0588 0.0588 0.3588
10 2.0000 8.0000 2.0086 8.0560 8.2462 8.3026 -0.0564 0.0564 0.6841
11 7.3078 9.9286 7.1856 9.8902 12.3281 12.2249 0.1031 0.1031 0.8365
12 8.8877 4.5526 8.8517 4.5102 9.9859 9.9345 0.0513 0.0513 0.5142
13 9.7993 3.2141 10.0822 3.2759 10.3129 10.6011 -0.2881 0.2881 2.7937
14 9.9281 3.2314 9.6102 3.1226 10.4407 10.1048 0.3360 0.3360 3.2178
15 11.0571 8.6505 10.7716 8.4157 14.0389 13.6694 0.3695 0.3695 2.6321
16 -3.0736 13.1967 -3.0663 13.2817 13.5499 13.6311 -0.0812 0.0812 0.5990
17 -8.0266 14.3605 -7.8907 14.2351 16.4515 16.2758 0.1757 0.1757 1.0679
RESULTRESULT
• Result for Dynamic nodes with Directed beam
07/22/15 ICGST 2012 Presented July 17

18. • Chart showing actual
distance and absolute
distance calculated
with algorithm
07/22/15 ICGST 2012 Presented July 17

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20. Real X Real Y
Calculated
X Calculated Y %Error
7.3078 9.9284 7.3078 9.9286 0.001639
7.3078 9.9287 7.3078 9.9286 0.000321
5 8.0002 5 8 0.001598
5 5.5001 5 5.5 0.001458
9.9999 8.0001 10 8 0.000257
9.9999 8 10 8 0.000231
14 8.5 14 8.5 0.00024
14 8.4999 14 8.5 7.66E-05
14 8.4999 14 8.5 7.66E-05
2 8 2 8 0.000136
7.3078 9.9286 7.3078 9.9286 0.000333
8.8878 4.5525 8.8877 4.5526 2.32E-05
9.7992 3.2142 9.7993 3.2141 0.000237
9.9281 3.2314 9.9281 3.2314 0.000412
11.0571 8.6505 11.0571 8.6505 2.6E-05
-2.4604 12.4981 -3.0736 13.1967 5.992092
-8.0265 14.3601 -8.0266 14.3605 0.002712
LATERATION WITH MOBILE
NODES
LATERATION WITH MOBILE
NODES
• Very high accuracy compared to
angulation technique
• Accuracy is lower that angulation
technique when implemented in an
actual system with noise present
07/22/15 ICGST 2012 Presented July 17

21. ANGULATION WITH NOISEANGULATION WITH NOISE
Measure
Distance Error %Error
11.7957847 0.491491 4.317584
12.0403254 0.50168 2.333973
8.98128915 0.37422 4.798525
6.60360613 0.27515 11.15868
11.6991752 0.487466 8.64479
12.2274814 0.509478 4.519411
15.6859003 0.653579 4.227773
15.5020319 0.645918 5.350405
15.6667858 0.652783 4.344479
7.97052162 0.332105 3.343228
11.7359324 0.488997 4.803081
9.53712999 0.39738 4.493644
10.1770087 0.424042 1.318059
9.70058861 0.404191 7.089097
13.1226023 0.546775 6.526824
13.0858203 0.545243 3.424999
15.6247409 0.651031 5.025155
Measured
Distance Error %Error
11.7115 0.616597 5.001557
11.71173 0.616367 4.999695
8.962443 0.471557 4.998482
7.061453 0.371547 4.998615
12.16592 0.640279 4.999756
12.16586 0.640338 5.00022
15.55942 0.818878 4.999772
15.55937 0.818927 5.000073
15.55937 0.818927 5.000073
7.833901 0.412299 4.99987
11.71166 0.616444 5.000316
9.486607 0.499293 4.999978
9.797232 0.515668 5.000225
9.918706 0.521994 4.999608
13.33695 0.701948 5.000025
12.10108 1.448821 10.69249
15.6285 0.822999 5.002576
LATERATION WITH NOISELATERATION WITH NOISE
07/22/15 ICGST 2012 Presented July 17

22. • Future work will be the development of a 3 dimensional
algorithm for PL&T in an indoor situational awareness
system.
• Another part of the proposal will be nodal
authentications.
FUTURE WORKFUTURE WORK
CONCLUSIONCONCLUSION
• Dynamic movement of reference nodes improves the
accuracy of PL&T estimation
• Considerable improvement over the Autonomous time
and speed movement to estimate the range of the robot
from its previous position used by the senior design
group
• Offers error as low as 0.24 percent in a noiseless system.
This error increases when with introduction of noise
into the system
• Unbounded AOA, error has ben proven to be as high as
7.46 percent
07/22/15 ICGST 2012 Presented July 17

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