UNDERWATER OBJECT DETECTION AND TRACKING
USING ELECTROMAGNETIC WAVES
PRESENTED BY
MUSBIHA BINTE WALI
STUDENT ID: 0906022
THESIS SUPERVISOR
DR MD. FARHAD HOSSAIN
DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING
BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY
DHAKA – 1000
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Outline • Overview • Motivation • Background • Related Works • My Work • The Proposition • Overall Localization Scheme • The Proposed Architectures • Performance Metric • Simulations, Results and Discussions • Conclusions • Summary of Contributions • Future Research • References
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Overview • 70% of the Earth’s surface covered by water, a resourceful domain.
• Active research for over a decade to explore it. • Sensor technologies’ acceptance: Underwater wireless sensor networks (UWSNs) being considered a tangible, low-cost solution. • Purposes served by the UWSNs: environmental monitoring, tactical surveillance, search and rescue missions, gathering of oceanographic data, marine archaeology, mine reconnaissance, disaster prevention, oceanography and many other aquatic applications. • Available transmission media for underwater environment: electromagnetic (EM), acoustics, optical. The main shortcoming of EM: the high absorption of in water, especially in seawater. Limited bandwidth (0 b/s to 20 kb/s) and impact on marine life are the problems of acoustics. Optical wave’s requirement of line-of-sight (LOS) limits its use in underwater environment. • UWSNs have, till now, almost exclusively been implemented using acoustic systems. Acoustics is proven technology to be the best engineering solution. • Re-evaluation of EM based communication underwater.
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Motivation • Slow transmission by acoustics,
increasing number of fast moving underwater vehicles and autonomous weapons used worldwide,
urgency for an alternative faster transmission media like optical or electromagnetic (EM) wave for detection and tracking purposes.
• Optical waves are impractical for major underwater applications .
• How to exploit the EM wave’s fast transmission capability in underwater to make real-time high-throughput applications feasible.
• Electromagnetic (EM) wave based underwater networks have great potential for supporting high-speed data rates in such scenarios because fast detection and transmission; immunity to acoustic noise, turbidity and pressure gradients; reduced impact on marine life and capability of non-LOS (NLOS) communications, which acoustic networks fail to afford, can be materialized by using EM based UWSNs.
• There needs more attention on underwater intruder localization networks based on a faster media other than acoustics.
• Therefore, this thesis proposes and investigates three dimensional (3D) cluster-based UWSN architectures for localizing underwater intruders.
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Background • Wireless Communications
• Underwater Wireless Communications and Networks
• UWSNs
• EM Wave Underwater Propagation Models
• Localization Methods
• Underwater Localization
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Wireless communications : • The fastest growing segment of the communications industry. • Wireless Network Challenges: Path Loss, Shadowing and Multipath fading,
Doppler Power Spectrum and Inter Symbol Interferences (ISI)
Mathematical Models of Wireless Channel: • Additive noise channel • Linear filter channel • Linear time-variant (LTV) filter channel
Figure: Additive noise channel.
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Underwater Wireless Communications and Networks: Underwater wave propagation, the challenges are much more acute than that on terrestrial. Comparison of Acoustic, EM And Optical Waves in Seawater Environments
Method Benefits Limitations
EM • short-range wireless communication using EM waves in
seawater has seen certain breakthroughs
• Crosses air/water/seabed boundaries easily
• Prefers shallow water
• Unaffected by turbidity, salinity, and pressure gradients
• Works in non-line-of-sight (NLOS); unaffected by
sediments and aeration
• Immune to acoustic noise
• High bandwidths (up to 100 Mb/s) at very close range
• Susceptible to EMI
• The main shortcoming stays with the high
absorption of EM waves in water, especially in
seawater.
Acoustics • Proven technology to be the best engineering solution in
most long-distance applications.
• Long Range (up to 20 km)
• Strong reflections and attenuation when
transmitting through water/air boundary
• Poor performance in shallow water
• Adversely affected by turbidity, ambient noise,
salinity, and pressure gradients
• Limited bandwidth (0 b/s to 20 kb/s)
• Impact on marine life
Optical • Ultra-high bandwidth (gigabits per second)
• Low cost
• Does not cross water/air boundary easily
• Susceptible to turbidity, particles, and marine
fouling
• Needs line-of-sight
• Requires tight alignment of nodes
• Very short range under water
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Underwater Wireless Communications and Networks: Underwater wave propagation, the challenges are much more acute than that on terrestrial. Comparison of Acoustic, EM And Optical Waves in Seawater Environments
Acoustic EM Optical
Nominal speed(m/s) ∼ 1,500 ∼ 33,333,333 ∼ 33,333,333
Power Loss > 0.1 dB/m/Hz ∼ 28 dB/1km/100MHz ∝ turbidity
Bandwidth ∼ kHz ∼ MHz ∼ 10-150 MHz
Frequency band ∼ kHz ∼ MHz ∼ 1014–1015 Hz
Antenna size ∼ 0.1 m ∼ 0.5 m ∼ 0.1 m
Effective range ∼ km ∼ 10 m ∼ 10-100 m
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Underwater Wireless Sensor Networks (UWSNs)
Typical Sensor Node. Sensor Node for
Underwater Environment.
Sensor Node Receiver Sensitivity
Receiver’s minimum operational sensitivity, Smin = (S/N) min kTo B(NF) Typical values for Smin of some receivers: RWR (Radar Warning Receiver) : -65 dBm Pulse Radar: -94 dBm CW Missile Seeker: -138 dBm
(S/N)min = Minimum SNR needed to process(vice just detect) a signal NF = Noise figure/factor k = Boltzmann's Constant = 1.38 x 10-23 Joule/K To = Absolute temperature of the receiver input (Kelvin) = 290K B = Receiver Bandwidth (Hz)
Clustered Sensor Network.
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EM Wave Underwater Propagation Model
EM waves propagate slower in water than in the air. Because water contains dissolved salts and other matter, it becomes a partial conductor. The higher water’s conductivity, the greater the attenuation of radio signals.
Prec(dBm) = Pt(dBm) + Gt(dB) + Gr(dB) − Lpathloss(dB) For underwter environment,
pathloss, PL = 20×(log10e)×2π× (σ × f × 10−7) dB/m , where where f = transmission frequency (Hz) and σ = conductivity of water (S/m). Sea water has a high salt content, with an average conductivity of 4 Siemens/meter (S/m), fresh water conductivity is typically about 0.01 S/m.
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Different Methods for Target Localization
Localization
Technique
Summary and
Characteristics
Strength and Weakness Usage and Applicability
TOA
TOA Uses distance
information between
FT and MT.
One-way ranging requires perfect
synchronization, while two-way
ranging does not.
More common in cellular
networks.
TDOA
Difference between
TOAs in several FTs are
utilized.
Needs highly precise synchronization
between MTs, while not precise
synchronization between FTs and MTs.
More common in
wireless sensor networks
AOA
Uses the angle
information to
construct the
lines between MT and
FTs and use their
intersection to find MT
location
Requires new hardware (antenna
arrays).
This means additional costs and larger
node sizes.
More appropriate for FTs
rather than MTs due to
large size. Otherwise MT
size has to be able to
accommodate an
antenna array. More
accuracy than RSS
RSS Distance is estimated
based on the
attenuation introduced
by propagation of the
signal
from FT to MT.
RSS Distance is estimated based on the
attenuation introduced by propagation
of the signal from FT to MT. An
accurate propagation model is needed
for reliable distance estimation. It is
low cost due to most RX being able to
estimate RSS. MT mobility and channel
variation may yield large errors.
Since it has low-precision
characteristic, typically
used in applications
which require coarse
estimate
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Underwater Localization
The detection or localization of underwater objects from presents a difficult problem due to various factors such as variations in the operating and environmental conditions, presence of spatially varying clutter, and variations in target shapes, compositions, and orientation.
Submerged Target Localization: Popular Methods
• Cluster based UWSNs using sonar data.
• Underwater Robots: rapidly identify and communicate potential threats.
• Magnetic anomaly detection (MAD) systems: MAD goals include - Underwater Target localization, characterization and Target tracking: Submarine detection, Ships’ wreck detection, Mine detection, UXO detection, Buried drums detection.
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Related Works • Two dimensional (2D) submarines localization techniques using a distributed sensor network of binary-detection capability.
• 2D two distributed particle filter based object localization and tracking schemes using cluster based UWSNs.
• Using cluster-based UWSNs contributes to robust topology and their aptness in effective energy saving strategies is evident through the works on increasing networklife of UWSNs.
• More than one works on three-dimensional (3D) underwater target localization and tracking schemes using UWSNs. The ToA of the echo messages coming from the target is used for determining the distance from the sensor to the target. Then trilateration is utilized to calculate target’s position.
Nevertheless, all the aforementioned works are developed based on acoustic networks.
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My Work • Proposes and investigates five different 3D UWSN architectures for detection and localization of underwater intruders.
Each architecture consists of SNs, CHs, sink at water surface and an onshore BS.
• Localization accuracy: in terms of
normalized mean square error (NMSE) in distance estimation and square-root of mean square error (SRMSE) in direction estimation.
• Impact of various network parameters, such as node topology, network length and
detection threshold, on the system performance is evaluated and critically analysed
Novelty of this work To the best of our knowledge, there is no complete published work on underwater intruder or target localization networks based on EM communications. So in this aspect, this is the first initiative of it’s kind.
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CH: EM transmitter and receiver. Activation upon Prec ≥ Pth .
gathers local information from SNs and/or other CHs,
forward them to the sink. EM signal power PCH and frequency fCH
SN
CH
Detection zone of an SN
Surface sink
CH in the uppermost layer
CH-to-sink data transfer
Sink to base station
Intruder
Base station (BS)
Overall model of the proposed localization schemes:
SN: Low power sensor package and an omnidirectional EM transmitter.
Pressure sensor, Motion sensor, Metal detector, Passive sonar.
EM signal power PSN and frequency fSN.
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Proposed UWSN Architectures
Architecture A1
𝐷𝐶𝐸 = 1
2∆𝑥2 + ∆𝑦
2 + ∆𝑧2.
𝑥𝑒𝑠𝑡 =1
𝑁 𝑥𝑖
𝑁
𝑖=1
, 𝑦𝑒𝑠𝑡 =1
𝑁 𝑦𝑖
𝑁
𝑖=1
, 𝑧𝑒𝑠𝑡 =1
𝑁 𝑧𝑖
𝑁
𝑖=1
Y
X
Z
∆z
∆x
∆ y
Topology for architecture A1
∆x
∆z
∆y
(1/2) x √(∆x2 + ∆y
2 + ∆z2)
Building block
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Architecture A2 𝑥𝑖𝑛𝑡 =
1
𝑁 𝑥𝑖
𝑁
𝑖=1
,
𝑦𝑖𝑛𝑡 =1
𝑁 𝑦𝑖
𝑁
𝑖=1
,
𝑧𝑖𝑛𝑡 =1
𝑁 𝑧𝑖
𝑁
𝑖=1
Y
X
Z
∆z
∆x
∆ y
Topology for architecture A2
𝑥𝑒𝑠𝑡 =1
𝑁 𝑃𝑚𝑎𝑥 − 𝑃𝑖 𝑥𝑖𝑛𝑡 − (𝑃𝑖𝑛𝑡 − 𝑃𝑖)𝑥𝑖
𝑃𝑚𝑎𝑥 − 𝑃𝑖𝑛𝑡
𝑁
𝑖=1
𝑦𝑒𝑠𝑡 =1
𝑁 𝑃𝑚𝑎𝑥 − 𝑃𝑖 𝑦𝑖𝑛𝑡 − (𝑃𝑖𝑛𝑡 − 𝑃𝑖)𝑦𝑖
𝑃𝑚𝑎𝑥 − 𝑃𝑖𝑛𝑡
𝑁
𝑖=1
𝑧𝑒𝑠𝑡 =1
𝑁 𝑃𝑚𝑎𝑥 − 𝑃𝑖 𝑧𝑖𝑛𝑡 − (𝑃𝑖𝑛𝑡 − 𝑃𝑖)𝑧𝑖
𝑃𝑚𝑎𝑥 − 𝑃𝑖𝑛𝑡
𝑁
𝑖=1
Fine Tuning:
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Architecture A4
Topology for architecture A4
Z
∆z
∆x
∆ y
X
Y
CHs at the regular intervals of 2∆x, 2∆y and ∆z distance along X-, Y- and Z-axis respectively. Each CH is equipped with four directional receivers directed to the four coplanar SNs.
𝐷𝐶𝐸 = 1
2∆𝑥2 + ∆𝑦
2
𝑥𝑒𝑠𝑡 =1
𝑁 𝑥𝑆𝑁,𝑖
𝑁
𝑖=1
,
𝑦𝑒𝑠𝑡 =1
𝑁 𝑦𝑆𝑁,𝑖
𝑁
𝑖=1
,
𝑧𝑒𝑠𝑡 =1
𝑁 𝑧𝑆𝑁,𝑖
𝑁
𝑖=1
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Architecture A3
Topology for architecture A3
CHs at the regular intervals of 2∆x, 2∆y and 2∆z distance along X-, Y- and Z-axis respectively.
𝐷𝐶𝐸 = 1
2∆𝑥2 + ∆𝑦
2 + ∆𝑧2.
∆z
∆x
∆ y
Y
X
Z
𝑥𝑒𝑠𝑡 =1
𝑁 𝑥𝑖
𝑁
𝑖=1
,
𝑦𝑒𝑠𝑡 =1
𝑁 𝑦𝑖
𝑁
𝑖=1
,
𝑧𝑒𝑠𝑡 =1
𝑁 𝑧𝑖
𝑁
𝑖=1
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Architecture A5
Topology for architecture A5
CHs at the regular intervals of 2∆x, 2∆y and 2∆z distance along X-, Y- and Z-axis respectively. Each CH is equipped with eight directional receivers directed to the eight SNs.
𝐷𝐶𝐸 = 1
2∆𝑥2 + ∆𝑦
2 + ∆𝑧2.
∆z
∆x
∆ y
Y
X
Z
𝑥𝑒𝑠𝑡 =1
𝑁 𝑥𝑆𝑁,𝑖
𝑁
𝑖=1
,
𝑦𝑒𝑠𝑡 =1
𝑁 𝑦𝑆𝑁,𝑖
𝑁
𝑖=1
,
𝑧𝑒𝑠𝑡 =1
𝑁 𝑧𝑆𝑁,𝑖
𝑁
𝑖=1
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Performance Metric
𝑟 = (𝑥𝑒𝑠𝑡 − 𝑥)2+(𝑦𝑒𝑠𝑡 − 𝑦)
2+(𝑧𝑒𝑠𝑡 − 𝑧)2
𝑁𝑀𝑆𝐸𝑟 = 𝑟𝑖
2𝑀𝑖=1
𝑀 min(∆𝑥, ∆𝑦 , ∆𝑧)2
where M is the number of Monte-Carlo simulations.
𝜃 = 𝑐𝑜𝑠−1𝑧 − 𝑧𝑒𝑠𝑡𝑟, −90° ≤ 𝜃 ≤ 90°
𝜑 = 𝑡𝑎𝑛−1𝑦𝑒𝑠𝑡 − 𝑦
𝑥𝑒𝑠𝑡 − 𝑥, −180° ≤ 𝜑 ≤ 180°
𝑆𝑅𝑀𝑆𝐸𝜃 =1
𝑀 𝜃𝑖
2
𝑀
𝑖=1
, 𝑆𝑅𝑀𝑆𝐸𝜑=1
𝑀 𝜑𝑖
2
𝑀
𝑖=1
X
Y
Z
Estimated location of an intruder
(r, θ, φ) with respect
to (xest, yest, zest)r
Original system coordinates
Reference point shifted to (xest, yest, zest)
(xest, yest, zest)
(0, 0, 0)
Error in Estimation for a Static Intruder
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Performance Metric
𝑟𝑎𝑐𝑡𝑢𝑎𝑙 = 𝑥1 − 𝑥22 + 𝑦1 − 𝑦2
2 + 𝑧1 − 𝑧22
𝜃 = 𝑡𝑎𝑛−1𝑧𝑒𝑠𝑡1 − 𝑧𝑒𝑠𝑡2𝑥𝑒𝑠𝑡1 − 𝑥𝑒𝑠𝑡2
− 𝑡𝑎𝑛−1𝑧1 − 𝑧2𝑥1 − 𝑥2
, −180° ≤ 𝜃 ≤ 180°
∅ = 𝑡𝑎𝑛−1𝑦𝑒𝑠𝑡1 − 𝑦𝑒𝑠𝑡2𝑥𝑒𝑠𝑡1 − 𝑥𝑒𝑠𝑡2
− 𝑡𝑎𝑛−1𝑦1 − 𝑦2𝑥1 − 𝑥2
, −180° ≤ 𝜑 ≤ 180°
Error in Estimation for a Moving Intruder
𝑟 = (𝑥𝑒𝑠𝑡1 − 𝑥𝑒𝑠𝑡2)2+(𝑦𝑒𝑠𝑡1 − 𝑦𝑒𝑠𝑡2)
2+(𝑧𝑒𝑠𝑡1 − 𝑧𝑒𝑠𝑡2)2
X
Y
Z
(xest1, yest1, zest1)
(0, 0, 0)
(xest2, yest2, zest2)
At time t = 0 At time t = t
Intruder Travelling in the System
(x1, y1, z1)
(x2, y2, z2)
𝑁𝑀𝑆𝐸𝑟 = (𝑟𝑖 − 𝑟𝑎𝑐𝑡𝑢𝑎𝑙,𝑖)
2
𝑀(𝑟𝑎𝑐𝑡𝑢𝑎𝑙,𝑖)2
𝑀
𝑖=1
, 𝑆𝑅𝑀𝑆𝐸𝜃 =1
𝑀 𝜃𝑖
2
𝑀
𝑖=1
, 𝑆𝑅𝑀𝑆𝐸𝜑=1
𝑀 𝜑𝑖
2
𝑀
𝑖=1
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Simulation Setup
• 1000 independent simulations • dimension Dx = Dy = Dz is considered
• path-loss 20×(log10e)×2π× (σ × f × 10−7) dB/m , where where f = transmission frequency (Hz) and σ = conductivity of water (S/m). Typically σ = 0.01 S/m for fresh water and σ = 4 S/m for salty water environment. The second one is considered. • fSN = 6 kHz and fCH = 3 kHz • transmit power PSN = 100 mW and PCH = 1000 mW • network dimension Dx×Dy×Dz = 400×400×400 m3 and ∆x = ∆y = ∆z = 20 m • detection threshold Pth = -60 dBm
CH
per
SN
Each
CH
has
4
dir
ecti
on
al a
nte
nn
as
Each
CH
has
8
dir
ecti
on
al a
nte
nn
as
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Results
0 5 10 15 20 25 30 350
0.2
0.4
0.6
0.8
1
1.2
1.4
A1
A2
A3
A4
A5
CDF of estimation Errors
Distance, r (meter)
Pr
(Estim
atio
n E
rro
r
r)
A1
A2
A3
A4
A5
Absolute distance r
CH
per
SN
Each
CH
has
4
dir
ecti
on
al a
nte
nn
as
Each
CH
has
8
dir
ecti
on
al a
nte
nn
as
-100 -50 0 50 1000
0.2
0.4
0.6
0.8
1
A1
A2
A3
A4
A5
Angle, (degree)
Pr
(Po
lar
An
gle
)
A1
A2
A3
A4
A5
10 12
0.57
0.58
0.59
0.6
Polar angle θ
-100 -50 0 50 1000
0.2
0.4
0.6
0.8
1
A1
A2
A3
A4
A5
CDF of Azimuthal angle (phi)
Angle, (degree)
Pr
(Azim
uth
An
gle
)
A1
A2
A3
A4
A5
-5 0 5
0.48
0.5
0.52
0.54
A1
A3
A4
A5
Azimuthal angle φ 25
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Results
CH
per
SN
Each
CH
has
4
dir
ecti
on
al a
nte
nn
as
Each
CH
has
8
dir
ecti
on
al a
nte
nn
as
1 2 3 4 50
0.2
0.4
0.6
0.8
1
1.2
1.4
NM
SE
of E
stim
ate
d D
ista
nce
(m
2)
A3
A4 A5
A2
A1
1 2 3 4 50
10
20
30
40
50
60
70
SR
MS
E o
f A
ng
le (
De
gre
e)
Polar Angle () Azimuthal Angle ()
A1A5A4A3A2
Average
distance and direction estimation errors
in detecting intruders
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Results
CH
per
SN
Each
CH
has
4
dir
ecti
on
al a
nte
nn
as
Each
CH
has
8
dir
ecti
on
al a
nte
nn
as
400 500 600 700 800 900 1000 1100 1200 13000
0.5
1
1.5
2
2.5
3
A1
A2
A3
A4
A5
Length of the sides of the 3-D networks (m)
NM
SE
in
Estim
ate
d D
ista
nce
(m
2)
A1
A2
A3
A4
A5
400 500 600 700 800 900 10000
0.5
1
1.5 A1
A2
A3
A4
A5
Length of the sides of the 3-D network (m)
NM
SE
in E
stim
ate
d D
ista
nce
(m
2)
A1
A2
A3
A4
A5
(a) Pth = -90 dBm
(b) Pth = -60 dBm
400 450 500 550 600 6500.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
A1
A2
A4
Length of the sides of the 3-D networks (m)
NM
SE
in
Estim
ate
d D
ista
nce
(m
2)
A1
A2
A3
A4
A5
(c) Pth = -30 dBm
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Results for a Travelling Intruder
CH
per
SN
Each
CH
has
4
dir
ecti
on
al a
nte
nn
as
Each
CH
has
8
dir
ecti
on
al a
nte
nn
as
28
1 2 3 4 50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
NM
SE
r of E
stim
ate
d T
rave
lled
Dis
tan
ce
A1
A2
A4 A5
A3
1 2 3 4 50
5
10
15
20
SR
MS
E o
f A
ng
le (
de
gre
e)
Polar Angle
Azimuthal Angle A1 A2
A3
A4
A5
NMSE and SRMSE in distance estimation and
travel direction estimation
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Performance in Intruder Detection with shadowing
CH
per
SN
Each
CH
has
4
dir
ecti
on
al a
nte
nn
as
Each
CH
has
8
dir
ecti
on
al a
nte
nn
as
29
for SN-CH communications, signal power received by the CH is
Prec (dBm)= PSN (dBm) –Pathloss (dB/m) × Distance + X (dB)
Where shadowing, X ~ N(0 , SD)
0 5 10 15 20 25 30 35 400
0.01
0.02
0.03
0.04
0.05
0.06
Shadowing SD
Pr
(No
De
tectio
n)
for
Ne
two
rk L
en
gth
60
0m
A1
A2
A3
A4
A5
0 5 10 15 20 25 30 35 400
0.2
0.4
0.6
0.8
1
Shadowing SD
Pr
(No
De
tectio
n)
for
Ne
two
rk L
en
gth
12
00
m
A1
A2
A3
A4
A5
Dx=Dy=Dz=600m Dx=Dy=Dz=1200m
My
Wo
rk
30
Summary of Contributions • studied wireless communications, underwater wireless communications, target localization, underwater target localization, underwater EM wave propagation and finally proposed five different EM wave based 3D UWSN architectures for the detection and localization of underwater intruders into a surveillance area. • The proposed architectures differ in topology and the principle of localization. Performance has been evaluated in terms of NMSE of estimated distance as well as SRMSE of polar and azimuthal angles. • Simulation results have demonstrated a great impact of network topology on the localization performance. • The utilization of information of received EM signal power and the location of SNs in location estimation can substantially improve the localization accuracy. This applies even in presence of underwater shadowing. • The integration of directional receivers in the CHs can achieve better accuracy even with deploying fewer CHs in the networks. Overall, the results have indicated that underwater target localization and tracking of the movement of the target using fast transmission media EM wave are achievable.
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Future Research • More complex network scenario including mobility,
underwater ambient noises and multi-path fading. • Improving the localization capability further by
integrating denoising filters and channel estimation, and using time-of-arrival (ToA) and angle-of-arrival (AoA) information.
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References [1] Andrea Goldsmith, “Wireless Communications” [2] J. G. Proakis and M. Salehi, “Communication Systems Engineering” [3] S. Al-Dharrab, M. Uysal and T. M. Duman, “Cooperative Underwater Acoustic Communications,” IEEE Communications Magazine, vol. 51, no. 7, pp. 146-153, July 2013. [4] R. Headrick and L. Freitag, “Growth of Underwater Communication Technology in the U.S. Navy,” IEEE Communications Magazine, vol. 47, no. 1, pp. 80-82, Jan 2009. [5] J. Partana, J. Kurosea, and B. N. Levinea, “A Survey of Practical Issues in Underwater Networks,” ACM Mobile Computing and Communications Review, vol. 11, no. 4, pp. 23–33, 2007. [6] S. Arnon and D. Kedar, “Non-Line-Of-Sight Underwater Optical Wireless Communication Network,” Journal of Optical Society America, vol. 26, pp. 530–539, Mar 2009. [7] X. Che, I. Wells, G. Dickers, P. Kear and X. Gong, “Re-evaluation of RF Electromagnetic Communication in Underwater Sensor Networks," IEEE Communications Magazine, vol. 48, no. 12, pp. 143-151, Dec 2010. [8] S. Zhou and P. Willett, “Submarine Location Estimation via a Network of Detection-only Sensors,” IEEE Transactions on Signal Processing, vol. 55, no. 6, pp. 3104–3115, June 2007. [9] Y. Huang, W. Liang, H.-B. Yu, and Y. Xiao, “Target Tracking Based on a Distributed Particle Filter,” Wireless Communications and Mobile Computing, vol. 8, no. 8, pp. 1023–1033, Oct. 2008. [10] G. Isbitiren and O. B. Akan, “Three-Dimensional Underwater Target Tracking with Acoustic Sensor Networks,” IEEE Transaction on Vehicular Technology, vol. 60, no. 8, pp. 3897–3906, Oct. 2011. [11] Z. Madadi, G. V. Anand and A. B. Premkumar, “3-D Source Localization in Shallow Ocean with Non-Gaussian Noise using a Linear Array of Acoustic Vector Sensors,” Proc. IEEE International Conference on Information Science, Signal Processing and their Applications (ISSPA), Montreal, QC, Canada, pp.1353-1358, July 2012. [12] W. Cheng, A. Y. Teymorian, L. Ma, X. Cheng, X. Lu and Z. Lu, “Underwater Localization in Sparse 3D Acoustic Sensor Networks,” Proc. IEEE International Conference on Computer Communications (INFOCOM), Phoenix, AZ, USA, pp. 13-18, Apr 2008. [13] N. M. A. Latiff, C. C. Tsimenidis and B. S. Sharif, “Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization," Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1-5, Sep 2007. [14] M. Younis, M. Youssef, and K. Arisha, “Energy-Aware Management for Cluster-based Sensor Networks,” Computer Networks, vol. 43, no. 5, pp. 649-668, Dec 2003.
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