Department of Electrical Engineering,
School of Science and Engineering,
Lahore University of Management Sciences, Pakistan
SONAR TRACKING OF INDUS RIVER DOLPHINS
BY
Student Name: Aneeq Zia
Roll No: 2012-10-0076
Student Name: M. Asif Rana
Roll No: 2012-10-0074
Student Name: M. Usama Bin Sikandar
Roll No: 2012-10-0080
Student Name: Talal Ahmed
Roll No: 2012-10-0073
A thesis submitted in partial fulfillment of the requirements
for the degree of
BS Electrical Engineering
School of Science and Engineering,
Lahore University of Management Sciences
Under the supervision of
Dr. Abubakr Muhammad
Designation: Director, Laboratory for Cyber-Physical Networks and Systems; Assistant Professor
of Electrical Engineering at LUMS
Email: [email protected]
TABLE OF CONTENTS
Acknowledgements…………………………………………………………………….…ii
Chapter 1: Problem Statement..…………………………………………………………..05
Chapter 2: Back Ground and Related Work…………………………………………..….06
Chapter 3: Design Methodology and Tools….…………………………………………...26
Chapter 4: Design, Simulation and Implementation Results……………………………..67
Chapter 5: Cost Analysis …………… …………………………………………………..68
Chapter 6: Conclusion and Future Recommendations.…………………………………..70
i
ACKNOWLEDGMENTS
The authors wish to express sincere appreciation to Dr. Abubakr Muhammad for their supervision
during the course of this thesis work and in the preparation of this manuscript. We are also very
thankful to WWF, Pakistan for assisting us in our field visits by providing us with transportation,
accommodation and other logistical support for data collection at Indus River near Sukkur, Sindh.
ii
Chapter 1
PROBLEM STATEMENT
Indus River Dolphins are one of the few river dolphin species in the world and an asset for
Pakistan. However, the rapid decrease in their population over the years has brought these
dolphins on the brink of extinction: When humankind came onto the scene, water infrastructure
development took place to irrigate our lands for agricultural purposes. Headworks
development restricted the movement of the dolphins. Therefore dolphins that were found in
all the five major rivers in Pakistan became restricted to only the Indus. Fisher-folk often
mistakenly caught dolphins in their fishing nets, and gradually commercial fishing replaced
subsistence fishing, leading to ever increasing competition between people and dolphins for
fish. Agriculture laden with pesticides and effluents from factories enter the river degrading the
quality of the habitat, and enter the food chain of both dolphins and people. Sometimes
dolphins wander into irrigation canals, leading from the river, and in winter when the canals are
closed for cleaning, they get stuck in small pools with no food, and die.
WWF-Pakistan has been involved in dolphin conservation for quite a few years now. However,
the methods employed by them for monitoring dolphin activity are based on human visualization
which is highly unreliable, and also limited in time and space. The need of the hour is to develop a
robust and reliable system to track and analyze the activities of these dolphins 24/7. A well
organized technical unit to rescue dolphins stranded in canals already exists, and
supplementing this unit with an electronic surveillance systems developed by us will help the
rescuers locate stranded dolphins, so that they can be rescued quickly.
5
Chapter 2
BACK GROUND AND RELATED WORK
2.1 BACKGROUND ON INDUS RIVER DOLPHINS
The Indus River dolphin (Platanista Gangetica minor) is one of the world’s most threatened
cetaceans. It is found only in the freshwater, in the mainstream of the Indus River and it is
endemic to Pakistan. Dolphins are found in primarily three subpopulations separated by irrigation
barrages. The dolphin populations are estimated to be 602 between Guddu-Sukkur barrages, 258
between
Taunsa-Guddu
barrages and
84
between
Chashma-Taunsa
barrages.
The
metapopulation was estimated to number approximately 1200 animals in 2001 (Braulik, 2006).
2.1.1 Problems Faced By Indus River Dolphins
The population of this species has gradually declined because of variousfactors, including water
pollution, poaching, fragmentation of habitat due to barrages, and dolphin stranding in the
irrigation canals. Numbers have dramatically declined since the construction of the irrigation
system in the Indus. Most individuals now remain in a 1,200 km stretch of the Indus River. The
Indus River Dolphin is listed as endangered by the U.S. government National Marine Fisheries
Service, under the U.S. Endangered Species Act.
6
2.1.2 How are the Dolphins Endangered
2.1.2.1. Lack of freshwater
Pakistan's huge irrigation scheme leaves very little water in the Indus River, particularly in
winter, for the dolphins to live in.
2.1.2.2. Entrapment in irrigation canals
Dolphins are sometimes trapped in narrow irrigation canals where they eventually die.
2.1.2.3. Fishing
Over and irregulate fishing can reduce the dolphin's food. Also dolphins can become entangled
in fishing nets in the river and drown.
2.1.2.4. Pollution
Chemicals in the Indus River System such as pesticides, organochlorines and industrial effluents
penetrate the dolphin's body. This reduces their physical fitness making them more prone to
illness and also causes reproductive problems.
2.1.2.5. Fragmented habits
The Indus Blind dolphins have been split into several small populations separated by impassable
barrages large dam-like structures across the Indus. These populations may be too small to
survive and may suffer from inbreeding that can result in reduced health or physical
deformities.
7
2.1.2.6. Hunting
In some places dolphin oil and body parts are sold and used as traditional medicine.
(Adventure Foundation of Pakistan)
2.1.3. Native Observation Methods
So far, visual observations are being used by WWF-Pakistan to observe the activities of Indus
River Dolphins. However, this method is quite unreliable and activity can be monitored only when
the dolphins come above the river surface to breathe. This is a tedious process of observation
that depends on visibility and physical proximity to the animals. It is also limited by the fact that
observations can be made during day-time only. Moreover the boats used in surveying dolphins
are not able to cover the entire river region. Due to these limitations, nothing much can be
known about dolphin activities. Furthermore, it is impossible to track the movement of individual
dolphins.
2.2. POSSIBLE SOLUTION
Acoustic Detection: small cetaceans such as dolphins and porpoises emit SONAR pulses that
they use as an echolocation tool for finding prey and for navigation. These sound pulses appear
as clicks of specified signal duration and bandwidth to an observer. These clicks can be
observed with specially designed underwater acoustic devices called hydrophones. The clicks
are usually emitted in a confined beam. It is proposed to devise a passive acoustic-based
method for survey of endangered cetaceans. These passive observation methods can also be
automated that allow day-night observation in turbid waters without causing any disturbance
to the animals.
8
We will make use of perpendicular linear arrays of hydrophones to detect the direction of arrival
of SONAR clicks. Further details on this method will be discussed later.
2.3. Study on Dolphin SONAR
The following are the classification of sounds produced by dolphins:
(i)
broadband, short-duration clicks, called SONAR clicks, used in echolocation for
orientation, perception, and navigation;
(ii)
wideband pulsed sounds, called burst pulses, used in social contexts
(iii)
narrowband frequency-modulated whistles also used in social contexts.
The dolphins use pulse trains as bio-SONAR. The number of clicks and the temporal interval
between successive clicks depend on several factors such as, for example, the distance from the
target, the environmental conditions, and the expectation of the animal on the presence/absence
of the prey. When the dolphin is in motion, the time that elapses between clicks often changes. A
train of clicks can contain from just a few clicks to hundreds of clicks. If the pulses repeat rapidly,
say every 5 milliseconds, we indifferently perceive them as a continuous tone. Generally, the
dolphin sends a click and waits for the return echo before sending the successive click. The time
elapsing between the reception of the return echo and the emission of a new click (lag time)
depends on the distance from the target.
Dolphins maybe can emit high-frequency clicks at low amplitudes, but cannot produce lowfrequency clicks at high amplitudes. Moreover, the dolphins can vary the amplitude of the bioSONAR in relation to the environmental conditions and to the distance of the target (Maria &
Fulvio, 2006).
9
Figure 2.1: Click train of Ganges River Dolphins (Tamaki et al, 2007)
Figure 2.2: Zoomed-in snapshot of a single click (Tamaki
et al, 2007)
10
2.4. The Dolphin SONAR System
Many things can be learnt from the biological SONAR system of dolphins which provide a source
of inspiration for many modern day acoustic tracking systems.
The dolphin’s lower jaw has been identified as part of an echo-receptor, and several hypotheses
have been proposed to explain this. In one of these, the regularity of dolphin teeth was
considered as a SONAR array. The teeth act as resonant pressure transducers, which are
combined as two equi-spaced line arrays with the tooth nerves introducing progressive
propagation delays, creating a delay line beam-former as shown schematically in figure 4.
Figure 2.3: The teeth of dolphin acting as an array
11
Figure 2.4: Schematic of the dolphin teeth array
The teeth in the jaws of bottlenose dolphins can be modeled as end-fire arrays diverging at an
angle in the order of 10-20 degrees. Propagation delays (τ in figure 2.4) are such that an echo
arriving from a direction along the row of teeth (boresight) results in all signals arriving at the CNS
simultaneously and adding constructively. Signals from other directions will not arrive
simultaneously, resulting in a reduced response. Thus, the array has directivity, with maximum
sensitivity in the boresight direction. The end-fire array has boresight in the direction along the
teeth array.
The unusual jaw of River Dolphinscan be modeled as a “Log-periodic array” as opposed an endfire array in bottlenose dolphins and other sea dolphin species. This is shown in figures 2.5 and
2.6.
12
Figure 2.5: Teeth of a river dolphin
Figure 2.6: Sketch of Log-periodic antenna
The log-periodic array configuration gives the river dolphins, added advantages over other
dolphin species:

Beam patterns change little over the frequency range

The beam patterns are essentially side-lobe free, giving better directionality (Dobbins
2007).
13
Figure 2.7: Near-field beam pattern of an end-fire array
Figure 2.8: Near-field beam pattern of a log-periodic
array
14
2.5. FREQUENCY OF SONAR CLICKS
The broadband SONAR clicks emitted by dolphins are thought to be exclusively used for
echolocation, the dolphin's amazing ability to gather information about its world through sound.
Clicks are produced in rapid sequence, called "click trains," that sound to us like a creaking door
or loud buzz. The frequency range for echolocation clicks is 0.25 to 220 kHz (Postolache et al.
2006).
According to Pilleri, the echolocation clicks of Indus River dolphins had a maximum frequency of
200 kHz. This information will later determine are minimum sampling rate while acquiring data on
PC.
2.6. DOLPHIN SOURCE PRESSURE LEVEL (SPL)
Table 3.1 provides a data shows the characteristic of different underwater species (Au pp. 133134). The parameters shown are: peak frequency(fp), 3-dB Bandwidth(BW), peak-to-peak source
level (SL) and signal duration(τ).
Focus on the column of Source Level (SL). This is the peak to peak pressure level produced by
species. Such readings are normally measured by tossing fish in front of the hydrophone and
recording the dolphin's signals as the dolphin echo ranges and swims towards the fish. This data
will determine the range of our system and the amplification required for a particular required
for a particular required range
15
Species
fp(kHz)
BW(kHz)
τ( s)
SL(dB)
Condition
Commerson’s dolphin
120-134
17-22
180-600
160
Tank
Hector’s dolphin
112-130
~14
~140
151
Sea
Beluga whale
100-115
30-60
50-80
225
Bay
Common dolphin
23-67
17-45
50-150
-
Sea
Pilot whale
30-60
-
-
180
Tank
Amazon River dolphin
95-105
-
200-250
-
River
Pacific
30-60
-
-
180
Tank
100-120
37
-
156
Tank
40
27
250
218
Sea
14-20
~4
210
178
Tank
Harbor porpoise
120-140
10-15
130-260
162
Tank
Dall’s porpoise
120-160
11-20
1801-400
170
Tank/Sea
65
10-180
40
150-180
River
100-130
15-40
100-120
228
Bay
5-32
-
-
-
Tank
white-sided
dolphin
Chinese River Dolphin
Narwhal
Killer whale
Ganges River Dolphin
False Killer Whale
Rough-toothed dolphin
Table 1: Properties Bio-SONAR of different species
16
2.7. GANGES RIVER DOLPHIN – THE CLOSEST SPECIES TO INDUS RIVER DOLPHIN
The Indus River dolphin swims on its side. As it swims it trails a side flipper along the bottom of
the river and moves its tail form side to side like a fish. This behavior enables the dolphin to swim
in water as shallow as 30cm deep. The dolphin needs to breathe after every 60 to 160 seconds. It
swims to the surface, rotates upright to take in air through its blowhole, and then rotates 90
again as it swims back to the bottom. This unique swimming behavior is not seen in any other
dolphin except the Ganges River Dolphin. (Adventure Society of Pakistan)
Moreover, Ganges River and Indus River dolphins belong to the same species of dolphins (i.e.
Platanistagangetica). Therefore, we can expect quite a few similarities among them. The Ganges
river dolphin's beam pattern is estimated to be very narrow so that the array would not record
clicks when the dolphin is not facing towards the array. The clicks of the Ganges River Dolphins
have been shown earlier in figures 2.1 and 2.2.
Although there is quite a lot of information on dolphins in general, but there is very little known
about the Indus River Dolphins. We can use these statistics as an estimate of the characteristics of
our specific dolphin.
2.8. THEORY BEHIND SONAR LOCALIZATION
The method for detecting Direction of Arrival of underwater SONAR is essentially the same as the
localization techniques for audible sound in air.
However, the primary difference between audible sound localization and SONAR is that SONAR
localization uses hydrophones instead of microphones. Hydrophones are basically pressure to
17
voltage transducers. Pressure levels are usually denoted in dB with respect to 1µPa pressure (dB
re 1µPa)
(2.1)
The hydrophone sensitivity is measured in the units of
hydrophone has a receiving sensitivity of
To explain this, if a
re 1µPa, it should
give1V output for a 187dB pressure relative to 1µPa (Madsen).
2.8.1. The Uniform Linear Array (ULA)
Figure 2.9: Uniform Linear Array of Microphones
The direction of arrival (DOA) of sound coming from a point-source can be calculated by a
uniform linear array of microphones/hydrophones. The sound arriving at M1 and M2 can be
represented as following,
18
(2.2)
(2.3)
where
is the sound waveform, τ is time-delay and
and
represent uncorrelated
noise at the microphones.
The time-delay, τ, of sound arriving at one microphone with respect to its adjacent microphone in
the array can be used found from the inputs obtained at the microphones and can be used to
calculate the angle from which the sound is coming.
Multiple microphone/hydrophone arrays can be placed perpendicular to each other to calculate
angles in the three-dimensional space.
2.9. COMPUTING THE ANGLE OF ARRIVAL
2.9.1. Filtering the received signals
Before implementing any time-delay estimation algorithms, the signal received has to be
separated from background noise and signals from other sources
2.9.2. Time-delay Estimation
The two signals attained at the two microphones/hydrophones in an array are cross-correlated.
The time at which the peak of the correlation function occurs, is used to calculate to the timedelay as follows:
19
Figure 2.10: Cross-correlation of sound received at two
microphones/hydrophones
–
(2.4)
whereT is the time-period of the sound wave and to is the time at which max of correlation
function occurs
Once the time delay is calculated, it is used to calculate the angle of arrival
using the following
formula
(2.5)
Care must be taken while deciding the distance (d) between microphones as it should satisfy the
following inequality:
(2.6)
20
where λmin is the smallest wavelength present in the arriving sound signal.
Other algorithms can also be applied for time-delay estimation
2.9.3. Visualizing in 3-D Space
The
following
figures
shows
the
Direction
of
Arrival
(DOA)
estimated
by
two
microphone/hydrophone arrays placed perpendicular to each other. These two arrays measure
the angles
and φ. Adding another perpendicular array would allow complete 3-D picture in a
sphere rather than the hemisphere shown in Figure 2.12 (Kirkwood 2003).
Figure 2.11: Two perpendicular linear arrays
21
Figure 2.12: The angles in space
The following features are considered to increase the precision of the DOA estimation:

Increasing number of hydrophones

Decreasing the distance between adjacent hydrophones

Increasing the sampling rate
2.10. MODELS FOR ARRAY CONFIGURATION
Three different configurations have been found which are pertaining to this project:
2.10.1 As used in Japan:
A vertical linear 3-hydrophone array forms a LBL system shown in fig. 2.13. H1,
22
H2 and H3 form a vertical array for localization in z-axis. Two more hydrophones H4 and H5 form
a triangular horizontal array with H2 for localization in x-y plane. It uses a total of 5
hydrophones.(Ura et al. 2006)
Figure 2.13 Japan configuration
2.10.2. As used in Ganges River
This made use of 6 hydrophone cross-arrays: 5 hydrophones in horizontal plane and 1 coming
out of the plane. The distance between hydrophones was kept at 0.8m (Sugimatsu et al. 2009).
23
Figure 2.14: Ganges River configuration (Sugimatsu, Ura,
Kojima, Bahl, & Behera, 2008)
2.10.3. As used in Chilika Lagoon:
Considering the features of Chilika lagoon, five hydrophones were configured as two
orthogonal three hydrophone SBL systems, each having a common central hydrophone and
two peripheral hydrophones. The hydrophones were kept 1.6m apart (Inoue et al. 2007).
24
Figure 2.15 Chilika Lagoon configuration (Inoue et al, 2007)
2.10.4. Conclusion on the Model to be selected
The model to be employed depends on the depth of the water in the river/barrage. For shallow
waters, the 5 hydrophone system of Chilika lagoon would suffice. However, for deeper waters,
we also need to localize the dolphin in z-direction as well, so we will need to employ the 6hydrophone system of Ganges River. We will be provided instruments to measure depth by
WWF, so that we may know the average depth of the river/barrage where we are supposed to
setup the system. Only then we can decide on the requirement of an additional hydrophone.
However, at least 5 hydrophones are necessary for precise localization.
25
Chapter 3
DESIGN METHODOLOGY AND TOOLS
3.1. INITIAL SYSTEM DIAGRAM
Figure 3.1: Initial Design Overview
Under-water equipment:
o Hydrophones
o Head Amplifiers
Water-based equipment:
o Data-Acquisition card (sealed)
26
o CPU
o Batteries for power supply
o Antenna for wireless communication with land-base (Optional)
Land-based equipment (Optional):
o Antenna for wireless communication with water-base
o Wireless LAN
o PC
The clicks obtained at the hydrophones are sampled, amplified and fed to water-base computer
placed nearby. The computer does the signal processing work to calculate the direction of arrival
angle of the dolphins and stores the information. The water-base computer is connected
wirelessly to the base-station computer. The base-station computer may be connected to the
internet to give around the world access to the administrator.
3.2. DESIGN OPTIONS WITHIN EACH PROJECT AREA
3.2.1 Hydrophones:
The hydrophones required should be able to cover the required frequency range of up to 180 kHz
and the frequency response of all the hydrophones should be as similar as possible so that crosscorrelation can be carried out efficiently. Furthermore, each hydrophone should be able to give
sufficient voltage in response to the dolphin Source Level within a sufficient range.
Available hydrophones have a receiving sensitivity of 187dB re 1V/μPa with a 20dB pre-amplifier
built-in, which means it will give 10V output at 187dB re 1 μPa pressure. Incorporating
27
underwater attenuation as well, which amounts to 20dB/m by the inverse-square law, we
expected to have a nominal voltage output within a range of about 10m.
The graph below shows the difference in the frequency response of the two hydrophones. We
can see that the difference is pretty negligible as compared to the receiving sensitivity of 187dB.
Figure 3.2 Frequency responses of the hydrophones
28
3.2.2 Data Acquisition
Since our objective in the end is to carry-out cross-correlation, our work is heavily
dependent on time-delays between acquired samples at the adjacent channels. For such
an application, it would have been ideal to have a DAQ which can simultaneously sample
each channel and that too at the sampling rate of at least 500kS/s per channel (to prevent
aliasing). Two DAQs proposed in this regard were: NI PCI-6123 and NI USB-6356. However,
since our working frequencies are still not very large, we can expect to have fair crosscorrelation results even without a simultaneous sampling DAQ already available to us.
The dolphin SONAR signals are to be received using hydrophones (underwater acoustic
sensors), which are first amplified and then sampled using NI PCIe-6251 Data Acquisition
Card which has aggregate sampling rate of 1MS/s (just enough to sample 2 hydrophones
available right now, without aliasing). The card is interfaced with a PC in which data is
acquired for further processing through LabView.
Overflow of data acquisition buffer because of high-speed sampling and recordingwas a
major cause of our inability to record dolphins’ SONAR signals on our trip to Sukkur during
last semester. For this, we had to do low-level programming in LabView to increase the
read/write speed of data acquisition:
29
Figure 3.2: Low-level VI in LabView
3.2.3 Amplification
Although, hydrophones already have 20dB pre-amplifiers built-in, but to be on the safer
side, we built external amplifiers as well. For this purpose, high bandwidth amplifiers with
flat frequency response in our required spectrum are preferable. However, due to the
non-availability of such amplifiers locally, we decided to use multiple UA741 general
purpose op-amps connected in series. UA741 ICs, with non-inverting input and feedback
configuration, are used to amplify the signal from each hydrophone.
3.2.4 Signal Classification Algorithm
Since our frequency spectrum of concern is in 10 k-110 kHz range, we may have separated
Dolphins’ Signals from other signals by using simple High-Pass filtering. However, there
was still a possibility of background noise and signal interference from other creatures at
30
such frequencies. In that case, we would have required Supervised Machine Learning
algorithms to differentiate dolphin acoustics from the incoming raw signal at the
hydrophone(s).
To get hands-on experience with Machine Learning algorithms in preparation for building
a Signal Classification system for the Dolphin Tracking System, we tested the different
Machine Learning algorithms on the gunshot signal database that was available with
CYPHYNETS group. For this, we considered2 possible Classification Algorithms: Bayesian
Learning and Support Vector Machines. SVM offers high accuracy, nice theoretical
guarantees regarding over fitting, and with an appropriate kernel they can work well even
if you’re data isn’t linearly separable in the base feature space. On the other hand, if the
conditional independence of Bayesian Classifier actually holds, a Bayesian classifier needs
less training data. In the process, we were able to analyze the pros and cons of different
Classification algorithms.
The Signal Classification algorithms for the gunshot database were written in MATLAB. The
relevant simulation results are given in the next section.
3.2.5 Localization Algorithm
We are using Generalized Cross-Correlation (GCC) algorithm which used time delay of
arrival between each of the hydrophones and the reference hydrophone to find the
dolphin location. In order to improve robustness against reverberation, we may use
Generalized Cross Correlation with Phase Transform (GCC-PHAT) as a localization
algorithm (if results from GCC only didn’t turn out to be satisfactory).
31
3.3 FINALIZED DESIGN
3.3.1 Factors Influencing the Choice of Final Design and Comparison of Designs
o The availability of just 2 hydrophones right now is a small constraint as complete 3-D
localization cannot be carried out. Moreover, 2-hydrophone system will also be
inaccurate in detecting angle in 1-D as well. However, this amount of hydrophones
will suffice for initial testing purposes. More hydrophones can be ordered later on.
o High-bandwidth amplifiers with large flat frequency response are not available in
local and/or international markets. Therefore, we have to use some alternative
approach to carry-out further amplification of the signals. The best alternative is to
make a circuit composed of several UA741 op-amps in series. We can set each opamp at low gains and hence get flat response for a large frequency range. With
multiple op-amps connected in series we can have a high cumulative gain without
losing on the frequency response part.
o A DAQ with capability of simultaneously sampling each channel is the ideal choice
for such application where time-delay is the main concern. However, the DAQ
currently available (without simultaneous sampling capability) may suffice for our
task because delays may still be ignorable.
o After visiting the site in Sukkur, we have concluded that it will not be possible to
implement separate land-base and water-base due to the large distance between
the deep river and the land. Hence, we have decided to setup the entire system on a
boat and keep it mobile rather than fixing it at the location.
32
3.3.2. Final System Diagram
Figure 3.4 Final Block Diagram
The data is acquired from two hydrophones through their separate channels, amplified and then
high-pass filtered digitally in LabView. Then, the cross-correlation algorithms are run on the data
acquired, and the angle of arrival of the incoming signal is estimated, shown on the dial in Figure
3.5 on the next page. For a particular configuration, distance between the hydrophones and the
sampling rate are set by the user.
33
Figure 3.5: Target Tracking Software Interface
3.4 PROBLEMS FACED IN FINALIZED DESIGN SELECTION, AND THE ADOPTED SOLUTIONS
3.4.1 Failure to Acquire Data in Trip 1
We conducted our first trip to Sukkur to understand dynamics of the location and the frequency
of dolphin sightings. Along with this, we wanted to have an exposure to the on-site issues and
with the data-acquisition system that we developed in the lab (using pinger as the ultrasound
source).
34
The main aim of the trip which was to get acquainted with the site was very much fulfilled.
However, the secondary aim, which was to acquire some initial data on dolphin clicks, could not
be achieved for a couple of reasons. The two main reasons for that were the data acquisition
system and the lack of enough amplification of the signal. Our data acquisition system did not
allow high-speed sampling which was required for the dolphin clicks. The lack of enough
amplification resulted in the signal being too low in amplitude to be differentiated from noise.
Other than these, many other logistical issues, such as not having proper power supplies,
insufficient RAM of PC used, resulted in our return from the trip without any valid data on dolphin
clicks.
3.4.2 Amplifier for Ultrasound Pinger:
The Direction of Arrival estimation algorithm has a limitation. That limitation is that the distance
of the acoustic source from the hydrophone assembly should be much greater than the distance
between the hydrophones themselves. Therefore, for purposes of testing the algorithm, we had
to have an ultrasound pinger with capability of producing ultrasound of sufficient range.
However, the Ultrasound Pinger (WBT-30) we had available to us required large input voltages
(comparable to 500V) to produce ultrasound of sufficient power and range. The function
generators available to us have peak output amplitude of 20V. So long-range ultrasound
generation required sophisticated power amplifier design which was beyond the scope of the
project. To add to the problem, the amplifier was also required to be able to work at high
frequencies. We contacted the manufacturers of the pinger and searched the local market but all
in vain. So, in the end we built our own power amplifier. First we tried to build a transformer but
due to non-availability of high frequency ferrite cores, we had to leave that idea. We concluded
35
on making a simple power amplifier, which had a bandwidth of only 3-4 kHz but was able to
provide sufficient range for our testing purposes.
The design of the amplifier was made on the basis of the components readily available in the lab.
The amplifier was simply composed of a power NMOS (IRF640) with a VDD of 100V supplied from
two power supplies in series. The drain resistance (Rd) was selected through trial error such that it
is comparable to the input resistance of the pinger at the frequencies of interest. Multiple
resistors were employed in parallel combination to give an effective of resistance of Rd; the
reason being that current can be divided among the resistors so that high current requirements
can be met without burning of resistors. A switching square wave of frequency 2 kHz and
amplitude 20V was input to the NMOS gate. The pinger input was taken from the drain output of
the NMOS.
Figure 3.6 Amplifier for the pinger
3.4.3 Preparing for the Trip 2
After coming back from the first trip to Sukkur without any data on dolphin clicks, we made quite
a few changes in our overall system. The required alterations in the software (that interface DAQ
36
with laptop computer) to enable high-speed sampling and recording was done. Moreover, we
used the ultrasound pinger to test the data acquisition system in the LUMS duck pond for a
couple of times. Apart from checking just the accuracy of the data acquisition system, we also
tested our direction of arrival estimation algorithm, which was developed in LabView, in the duck
pond using the pinger as the ultrasound source. Moreover, we designed an amplifier of 35dB gain
with a bandwidth of 150kHz to add to the 20dB pre-amplifier built in the hydrophones. This was
necessary since the power level of the dolphin clicks (theoretically) at the beak was low enough
to prevent the hydrophones to acquire the correct signal using only it’s pre-amplifier.
Having made all the necessary changes in our system, we were now well equipped for the next
trip to Sukkur. The main aims set for the next trip to Sukkur were to acquire dolphin’s SONAR
signal and test our system to localize dolphins within a semi-circle. An added objective was, if we
had time, to test the working of our basic classification system.
Fortunately, in our second trip, we were able to record underwater sounds at a sampling rate of
1MS/s with a single hydrophone at a depth of 2m. The data for direction of arrival estimation was
recorded at 500 kS/s at each channel for the two hydrophones. Since the dolphin population was
quite high in our working site (around 3-4 sightings in every 30 second interval), we were sure of
having recorded enough data for further work.
Unfortunately, towards the end, our amplifier circuit stopped working after 30-40 minutes while
we were recording data with two hydrophones. And since we didn’t have any backup, we had to
stop the recordings there. But in any case, since the occurrence of clicks was very fast, even the
data of 10 minutes was enough to test our DOA estimation algorithm.
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Chapter 4
DESIGN, SIMULATION AND IMPLEMENTATION RESULTS
4.1. SIMULATION METHODOLOGY AND RESULTS
4.1.1 Simulation Methodology for Machine Learning Algorithms
Since we do not have recordings of Indus river dolphin’s sonar signal so far, we simulated the
proposed Supervised Learning Algorithms (Bayesian and Support Vector Machines) on Gunshot
dataset that was readily available. The dataset contained around 700 gunshot audio recordings.
We used spectrogram energies and Mel Frequency Cepstrum for feature extraction. For
classification, we used 8-hold cross-validation with both Bayesian Model and Support Vector
Machines, using the same set of features. We have used 434 gunshots to test the features using
8-hold cross-validation.
a) Bayesian Classifier
6 Features are used to define the Bayesian Classifier
Average accuracy rate in training subset = 95.23%
Average accuracy rate in testing subset = 74.31%
b) Support Vector Machines
38
i) 6 features are used to define the hyperplane
Average Accuracy Rate in Training Subset = 96.68%
Average Accuracy Rate in Testing Subset = 51.19%
ii) 2 features are used to define the hyperplane
Average Accuracy Rate in Training Subset = 78.58%
Average Accuracy Rate in Testing Subset = 70.64%
Note that False Positive cannot be defined in training subset in the case of Bayesian Classifier
since the model is built using gunshots only. Thus, the ‘average accuracy rate in training subset’ in
case of Bayesian Classifier may “appear” to be higher since outsider signals are not used for
testing the training subset.
4.1.2 Simulation Methodology for Localization Algorithms
The Cross-Correlation Algorithm was simulated in NI LabView. Two signals were simulated with a
pre-defined phase difference of 30 degrees between them. White Gaussian Noise of different
amplitudes and standard deviations was also added to make the signals close to reality. The two
signals were then fed into the cross-correlation block in LabView. The maximum of the crosscorrelation between the two signals provided the time-delay between them.
The simulation was run for around a million iterations and histograms were plotted. This was
repeated for Sinusoid, Triangular and Square-wave signals with noise standard deviations of 0.1,
0.3 and 0.6. The mean phase difference calculated by the algorithm was close enough to the predefined 30 degrees phase difference, with a nominal standard deviation.
39
Table 2: Simulation Results for Localization Algorithm
The histograms are shown below on the following pages:
40
Figure 3.1: For Square wave of frequency 10 Hz and
noise SD of 0.1
Figure 4.2: For square wave of frequency 10Hz and
noise SD of 0.3
41
Figure 4.3: For square wave of frequency 10Hz and
noise SD of 0.6
Figure 4.4: For triangular wave of frequency 10Hz and
noise SD of 0.1
42
Figure 4.5: For triangular wave of frequency 10Hz and
noise SD of 0.3
Figure 4.6: For triangular wave of frequency 10Hz and
noise SD of 0.6
43
Figure 4.7: For Sine wave of frequency 10Hz and
noise SD of 0.1
Figure 4.8: For Sine wave of frequency 10Hz and
noise SD of 0.3
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Figure 4.9: For Sine wave of frequency 10Hz and
noise SD of 0.6
4.2. INTEGRATED DESIGN SIMULATION
The diagram shows the simulation discussed earlier to test the Cross-correlation technique. As
described earlier, two signals are simulated at the frequencies specified and the phase difference
specified by the user. The simulation calculates the phase difference using the cross-correlation
algorithm for the number of iterations required. Histogram is then plotted and mean and
standard deviation is also calculated to provide a measure of accuracy of the algorithm.
Furthermore, we also emulate the noisy underwater conditions by adding white Gaussian noise
to the signals. The results of the simulation have been provided earlier.
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Figure 4.10: Simulation for Testing
4.3. ACQUIRED DATA
The data was recorded in the segments of 50MB lvm files through LabView. The signal was highpass filtered in the LabView and monitored throughout the recording. Snapshots of the 10k
samples window and its power spectrum from the LabView are shown on the following pages:
46
Figure 4.11: Snapshot of the Labview Interface
Figure 4.12: Impulse Train can be seen in the above figure. The PSD of the above window can be
seen in the second window.
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4.4. DATA ANALYSIS
4.4.1. Filtering the Data
The data that was recorded had a high amplitude low frequency noise in the audible range, as seen
in the figure below.
Figure 4.13: Unfiltered Dolphin Acoustics
Figure 4.14: Power spectrum of a typical dolphin click segment
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This noise needed to be filtered out. Moreover, as seen from the power spectrums of multiple
data segments, the spectrum of dolphin sonar starts around 20kHz and lasts till 100kHz.
Therefore, so as to extract the dolphin sonar from the signal effectively, a bandpass filter was
designed using ‘fdatool’ in Matlab. Filter has the following characteristics:
Lower cutoff frequency = 20kHz
Upper cutoff frequency = 100kHz
Design Method: FIR Equiripple
Order = 618
Stopband amplitude = -100dB
Passband amplitude = 0dB
Sampling Frequncy = 1M samples/s
The frequency response of the filter is shown in the following page:
49
Figure 4.15: Frequency response of the BPF used
The data to be analyzed can now be filtered not only for a better analysis, but also for the
effective working of the cross-correlation and the classification algorithms. A sample data
segment before and after applying the bandpass filter is shown in figure 4.16.
Figure 4.16: Raw and the Filtered Data
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4.4.2. Extracting the Clicks
Now, the objective is to divide each segment into sub-segments such that individual clicks can be
extracted from such sub-segments.
4.4.2.1. Method 1: Amplitude Thresholding
After analysis of a lot of segments, it was decided that 0.8ms was a good size of such a subsegment. So, the 2.6s segments were divided into 0.8ms sub-segments, and all the sub-segments
containing a maximum absolute datapoint of more than 0.6V amplitude were plotted, and their
indexes were also stored in an array. One such plot is shown in figure 4.17.
Figure 4.17: A pair of dolphin clicks in a 0.8ms chunk of recorded data
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4.4.2.1. Method 2: Variance-based Dolphin Click Detection
The method can be described by the following steps:

Filtering of the signal by passing it through a band pass filter with cut-off frequencies at
20 KHz and 100 KHz;

Estimation of the signal power, for each consecutive non-overlapping block of samples;

Windowing of the obtained power sequence to consider only its more recent elements;

Normalization of the windowed power sequence;

Determination of the variance of the resulting normalized sequence;

Application of a threshold on the variance for making the detection decision.
The output from various stages of the algorithm is shown on the few next pages:
52
Stage 1: Filter the raw signal using a band pass filter with cut-off frequencies at 20 KHz and 100
kHz
Figure 4.18: Filtered Signal with its DFT
53
Stage 2: Implement the variance-based impulsive event detection algorithm on the filtered
signal. The variance of each chunk of pre-defined length is plotted below w.r.t. the chunk
number.
Figure 4.19: Filtered Signal with the variance of respective chunks
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Stage 3: Put a threshold on the variance to extract chunks containing the clicks. Sample
extracted dolphin clicks are given in figure 4.20.
Figure 4.20: Extracted Click via the impulsive event detection algorithm
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Comparison between the 2 Click Detection Methods:
1. Amplitude-based Click Detection
a. Pros
i. Lower False Detection Rate
ii. Lower Computational Cost
iii. Simple to implement
b. Cons
i. Difficult to detect weak dolphin signal (distant dolphins)due to amplitude
thresholding
ii. Non-optimal solution – many dolphin clicks are likely to be missed out
2. Variance-based Click Detection
a. Pros
i. Detects weak as well as strong dolphin signals (i.e. detects distant as well
as nearby dolphins)
ii. Higher True Positive Rate
iii. Insensitivity to a possible positive or negative tilt of the background noise
level
b. Cons
i. Higher False Positive Rate
ii. Higher Computational Cost
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4.4.3. Click Analysis
Throughout most of the data following patterns were observed:
1. Recorded clicks appear in pairs, with an inter-click interval of around 0.3ms, as shown in
the figure below
Figure 4.21: 2 dolphin clicks with an inter-click interval of 0.3ms
2. In such a pair, the second click is always 1800 phase shifted as seen in the figure above.
This gives rise to the possibility that the subsequent click in such a pair is a reflection
from a hard surface. One of such click pairs was extracted and the individual clicks were
compared with each other. Their comparison is shown in figure 4.22.
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Figure 4.22: Two successive clicks in a click pair
If the subsequent is inverted (180o phase-shifted), as shown in figure 4.23, a much
better comparison can be conducted.
Figure 4.23: Two successive clicks invertedsubsequent click
58
There is of course a striking similarity between the two clicks. This similarity is seen
throughout the data. This confirms that the subsequent click is indeed a reflection from
a hard surface. The time interval between the original click and its reflection was 0.3ms
on average. Using this time delay, the distance between the two paths is calculated to
be around 50cm. The only hard surface in such a close proximity to the hydrophones
was indeed the base of the boat. So, the subsequent click might indeed be the reflection
from the base of the boat.
3. But if the subsequent click in a click-pair is considered a reflection then in many cases,
the amplitude of the subsequent click is indeed found to be larger than the first click,
posing a question mark to our previous conclusion. One such case is shown in the figure
4.24.
Figure 4.24: Reflected click having higher amplitude than the original click
59
Now, there can only be only possibility if the second click is really a reflection. As the
dolphin SONAR beam is very narrow and directive, it is possible that a situation arises in
which the hydrophone comes directly in the path of a reflected beam, but after
receiving the original, hence weaker beam. This situation is shown in figure 4.25.
Figure 4.25: A possible explanation for Fig. 4.24
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4. Additionally, there are reflection from the water surface that can be observed in the data,
but these are pretty feeble can be clearly differentiated from the original clicks, as consulted by
another research paper as well (R. Bahl et. al.). Such reflections are pointed out in figure 4.26.
Figure 4.26: A click with its reflections from boat surface and water surface
4.4.4 Discrete Fourier Transform of a Single Click
Several clicks were extracted from the recorded data and their DFT’s were plotted and analyzed.
Four such plots are shown in figure 4.27 on the next page.
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There is a variation between the peak frequencies of different clicks. The DFT’s of different clicks
can be seen to peak at 40kHz, 50kHz and even 60kHz. The variation may be due to the difference
in age and/or size and/or the sex of the dolphins.
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Figure 4.27 DFT’s of 4 Different Clicks
In all the frequency spectrums above, three high-frequency bands with considerable energy can
be noticed:
1. A dominant 20kHz to 100kHz band containing most of the signal energy
2. A lesser dominant 100kHz to 170kHz band
3. The least energy 170kHz to 220kHz band
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4.5. Direction of Arrival Estimation Algorithm Applied to the Recorded Data
For direction of arrival estimation, 3.6s segments recorded from both the hydrophones were
divided into smaller chunks of 1ms each. Then the chunks from both the hydrophones that
contained the dolphin clicks were extracted and cross-correlated with each other, in order to find
the time delay between the same click received by two hydrophones. The direction of arrival is
then calculated as shown in figure 4.28.
Figure 4.28 Direction of Arrival Estimation
But, with two hydrophones, there is an uncertainty. The
calculated for the dolphin position
shown in figure 4.28 would exactly be the same as that calculated in the situation shown in figure
4.29 on the next page. So, one can’t be sure whether the dolphin is located on one side of the
hydrophone array or the other.
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Figure 4.29 Same direction of arrival with a different dolphin location
4.6 DOLPHIN CLASSIFICATION
For a 3.6s data file, the directions of the dolphins were plotted on the circumference of a circle in
Matlab. A very basic mechanism to differentiate one dolphin from the other is also implemented,
based on the angular displacement of one dolphin from the other. In a window of 3.6s, if the
difference between a newly calculated direction and the already calculated ones in the same
window is greater than a particular pre-set value, then the new click is most probably from a
different dolphin. This was implemented on the basis of the reasoning that the dolphin is not able
to change its angular position too much in a timespan of 1ms (the time span of a chunk) to 3.6s
(the time span of the whole file), unless the dolphin is in a proximity of 0-3m to the hydrophones,
which is indeed a very rare case. That particular value of the angular displacement chosen was
200. So, for example, a click is detected and the direction is calculated to be 1200. If the next
detected click gives an angle of 1410 then this click is definitely from a different dolphin. Few such
results of angular position tracking are shown in figure 4.30. The uncertainty described in figures
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4.28 and 4.29 is also demonstrated, with two images of one detected dolphin shown on the
upper and lower half of the circle, depicting two possible positions of the dolphin.
Figure 4.30 Tracked angular positions of different dolphins, represented by different colors, in a time
span of 3.6s
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Chapter 5
COST ANALYSIS
Equipment Name
Quantity
Price
Hiriutc RHS-10 hydrophones
2
Rs. 120,000 (=60,000 x2)
NI USB-6536 Sim Sampling DAQ
1
Rs. 368,000
Pinger for hydrophone calibration
1
Rs. 60,000
Laptop/Computer
1
Rs. 50,000
Total Cost
Rs. 598,000
67
CHAPTER 6
CONCLUSION AND FUTURE RECOMMENDATIONS
6.1 CONCLUSION:
Small cetaceans such as dolphins emit SONAR pulses that they use as an echolocation tool for
finding prey and for navigation. These pulses aka clicks are produced in the frequency range of
0.25 to 220 kHz (Postolache et al. 2006). For dolphin detection, these clicks were acquired with
specially designed underwater acoustic devices called hydrophones, and were extracted from the
streaming data using a variance-based impulsive event detection algorithm. For localization of
dolphins within a semicircle, we used 2 hydrophones such that the time-delay of incoming
dolphin acoustic between the 2 hydrophones can be used to localize the source.
At the end of the project, we were able to achieve the following things:
1) Build a data acquisition system that can record low amplitude SONAR signals via
hydrophones
2) Acquire SONAR signals of Indus River Dolphins at Sukkur, Sindh using the data acquisition
system
3) Build a Signal Classification Software that can detect Indus dolphic’sboi-acoustic signal
4) Build a Target Tracking software that can localize the Indus River dolphins
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6.2 FUTURE RECOMMENDATIONS
In future, blind source separation should be incorporated to separate the incoming clicks from
different dolphins so that their respective trajectories can be plotted. The long-term aim is to
build a target tracking software that shows trajectories of all the dolphins present within a pre-set
radius.
In future, the water-based computer may be connected wirelessly to the base-station computer.
The base-station computer will do all the signal processing work and it may be connected to the
internet to give around the world access to the administrator.
The number of hydrophones used for localization should be increased beyond 2. At least 4
hydrophones are required by the system to localize a source within a sphere (currently, we are
able to localize an acoustic source within a semi-circle only).
Hydrophones may be manufactured locally at the CYPHYNETS group. If we are successful in doing
so, it will be possible to implement a sensor network of dolphin trackers at different places on the
river Indus.
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REFERENCES
Braulik, G. T. (2006). Comprehensive status assessment of the Indus River dolphin (Platanista
gangetica minor). Biological Conservation , 579-590.
Inoue et al, T. (2007). Long duration real-time observation of Irrawaddy dolphins in Chilika lagoon.
OCEANS. Tokyo.
Maria, G., & Fulvio, G. (2006). Analysis and Modeling of Echolocation Signals Emitted by
Mediterranean Bottlenose Dolphins. EURASIP Journal on Advances in Signal Processing , 1-10.
Sugimatsu, H., Ura, T., Kojima, J., Bahl, R., & Behera, S. (2008). Underwater behavioral study of
Ganges river dolphins by using echolocation clicks recorded by 6-hydrophone array system.
OCEANS, (pp. 1-7). Tokyo.
Tamaki et al, U. (2007). Estimated beam pattern and echolocation characteristics of clicks recorded
from a free-ranging Ganges river dolphin.
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