On Underwater Acoustic Communication & Source Localization

On Underwater Acoustic Communication & Source

Localization

Thesis submitted to the

School of Mechatronics & Robotics Indian Institute of Engineering Science and Technology, Shibpur

(Formely Bengal Engineering and Science University) Howrah- 711103

For award of the degree of

Master of Technology in Mechatronics by

Bipin Patel

Roll No: 191225008 Regn. No: 235512008 Under the supervision of

Shri. Siva Ram Krishna Vadali

& Shri. Sambhunath Nandy

School of Mechatronics CSIR- Central Mechanical Engineering Research Institute

Durgapur 713209

May 2014

APPROVAL OF THE VIVA-VOCE BOARD

Date: / /

Certified that the thesis entitled On Underwater Acoustic Communication & Source Localization submitted by Bipin Patel to the Indian Institute of Engineering Science and Technology, Shibpur, for the award of the degree Master of Technology has been accepted by the external examiners and that the student has successfully defended the thesis in the viva-voce examination held today.

(Board of Examiners)

...........................................

...........................................

...........................................

CERTIFICATE BY SUPERVISOR

This is to certify that the thesis entitled On Underwater Acoustic Communi- cation & Source Localization submitted by Bipin Patel to Indian Institute of Engineering Science and Technology, Shibpur, is a record of bona fi research work under my supervision and I consider it worthy of consideration for the award of the degree of Master of Technology. To the best of my knowledge, the results embodied in this thesis have not been submitted in any other University or Institutes for the award of any other degree or diploma.

(Shri. Siva Ram Krishna Vadali) Senior Scientist, CMERI, Durgapur

(Shri. Sambhunath Nandy )

Principal Scientist, CMERI, Durgapur

Countersigned by: School of Mechatronics & Robotics

Indian Institute of Engineering Science and Technology, Shibpur

(Dean of Faculty of Engineering and Technology)

Indian Institute of Engineering Science and Technology, Shibpur, Howrah

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Student Declaration

I hereby declare that the work presented in this project entitled On Underwater Acoustic Communication & Source Localization submitted towards com- pletion of fulfilment of the requirement of the Degree of Master of Technology at Indian Institute of Engineering Science and Technology, Shibpur is an authentic record of my work carried out under the guidance of Shri. Siva Ram Krishna Vadali & Shri. Sambhunath Nandy . The project was done in full compliance with the requirements and constraints of the prescribed curriculum.

Place: Durgapur Bipin Patel Date: / / Roll No. 191225008

Registration No.235512008

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Abstract

Covering almost 75% of the planet, the ocean is a vast, complex, mostly dark world, largely unknown and unexplored by man. Understanding the ocean and its behavior is important to scientists in diverse areas such as oceanography, seismic exploration, weather and climate monitoring, etc., and has barely been touched by todays science and technology. The ocean is essentially opaque to light and electromagnetic radiation but it is transparent to acoustic signals. Therefore, sound is the only practical way to propagate signals to great distances in the ocean. The propagation of sound in the ocean is of vital importance, not only for communication between marine animals but also for fi objects, measuring water depth, currents, or other environmental parameters. In order to accomplish all this task autonomous underwater vehicles (AUV) required and also AUV need to be capable of estimating their position within the environment and doing communication with base station. This is a prerequisite of a successful mission since further tasks that need to be achieved strongly rely on localization information and communication.

In the present work we take up two issues in underwater signal processing where the objectives under the diff t notes are as here under : In Underwater Acoustic Communication Perspective : (a) To model Underwater Channel Noise. (b) To Design a Underwater Communication link. (c) To Test Different Digital Modulation Schemes in Underwater ambience. In Underwater Acoustic Source Localization Perspective : We have used time delay estimation(TDE) method to localize the acoustic source in presence of impulsive noise and in presence of multipath environment. (a) TDE using Generalized Cross-Correlation Phase Transform GCC-PHAT. (b) TDE using signal detection based method. (c) Comparision of GCC-PHAT and signal detection based method for TDE. (d) Performance improvement of GCC-PHAT and signal detection based method for TDE using order statistics.

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The thesis work has been conducted in two phases comprising underwater com-

munications and acoustic source localization. Our main focus is on underwater localization. We have started with problem of underwater channel modelling and testing diff t communication technique. In autonomous underwater vehicle (AUV) context communication is the important means to achieve marine monitoring, data acquisition and strategic communications. The underwater channel is a dynamic and complex environment. Hence to avoid failure of real time underwater communication prediction of the behaviour of underwater acoustic channel is important.

After that we have taken the problem of acoustic source localization. Various localization algorithms have been proposed for terrestrial sensor networks, there are relatively few localization schemes for underwater localization. The characteristics of underwater channel are fundamentally diff t from that of terrestrial channel. Acoustic source localization in shallow water is commonly dominated by impulsive noise and multipath phenomenon. Traditionally for acoustic source localization with sensors spaced several wavelengths apart involves time delay estimation (TDE) via Generalized Cross-Correlation Phase Transform (GCC-PHAT). However multipath signals and impulsive noise in underwater ambience result in spurious peaks leading to anomalous TDEs, and in turn erroneous source location. In the present work we suggest two methods to improve TDE one based on Or- der Statistics and the other via detection of signals. Simulation results indicate a significant improvement in time delay estimation as compared to GCC-PHAT in presence of outlier data and in fading channels.

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Acknowledgement

I would like to take this opportunity to thank Director, CSIR-CMERI, Durgapur and Dean, School of Mechatronics & Robotics, IIEST, Shibpur for allowing me to carry out my thesis work at CSIR-CMERI, Durgapur.

This thesis would not have existed without the support and guidance of my supervisors Shri. Siva Ram Krishna Vadali & Shri. Sambhunath Nandy. They have inspired me a lot to contribute something towards the Underwater Sig- nal Processing arena. They have allowed me to think freely and motivate me as a researcher, teacher, writer and mentor. They have always inspired me by vision, mental support and enthusiasm, whenever I needed them. It has been a proud privilege for me to work under them.

I would also like to thank all my friends Anirban, Biswarup, Purnashis, Sumit, Susmita, Rijul, Surojit, Amit and Satodhal and seniors Spandan, Souvik and Jeet for helping me during my thesis work. Their help have been priceless in the course of my thesis work.

Finally, I dedicate this thesis to my parents and family members for their constant love, encouragement, support and for being behind me always.

Bipin Patel IIEST-CSIR-CMERI

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Contents

Frontmatter i

Approval of the Viva-Voce Board ........................................................................ ii

Certificate by Supervisor ..................................................................................... iii

Student Declaration ..............................................................................................iv

Acknowledgement ..................................................................................................... vii

1 Introduction 1

Why Underwater ............................................................................................... 1

Related Work .............................................................................................................. 2

Contribution of Thesis Work ................................................................................ 4

Thesis Organization ...................................................................................... 5

2 A Survey on Signal Processing for UWA Communication and Source Localization 6

Underwater Channel .................................................................................... 6

Channel Characteristics and Link Design Parameters ................ 6

Modelling Noise Behaviour in Shallow Water Environment . 10

Multipath Phenomenon in Underwater Channel ...................... 11

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A Brief note on Underwater Acoustic Communication .......................... 14

Coherent Modulation ..................................................................... 15

Acoustic Source Localization in Underwater Ambience......................... 17

Conclusion ................................................................................................... 27

3 Performance Analysis of Digital Modulation Schemes in UW Chan- nel 28

Link Design Analysis for a UWA Communication System ................... 28

Bit Error Performance of BPSK Modem in UW Channel . . 33

Bit Error Performance of FSK Modem in UW Channel . . . 34

Bit Error Performance of QPSK Modem in UW Channel . . 35

3.2 Conclusion 36

4 Proposed Method to Improve UWA Source Localization 37

Order Statistics Based Approach................................................................. 41

Order statistics ................................................................................ 41

Applications of Order Statistics .................................................. 42

Signal Detection Based Approach .............................................................. 42

Performance Improvement of GCC-PHAT in Presence of Impulsive Noise: Proposed Order Statistics Based Approach ................................ 44

Improved Time Delay Estimation: Proposed LRT based Approach . 47

Performance of GCC-PHAT and LRT based TDE: In Presence of Outliers when Order Statistics Incorporated ......................................... 50

Conclusion ................................................................................................... 52

5 Conclusion and Future Directions 53

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Work Done ................................................................................................... 53

Underwater acoustic communication ............................................ 53

Underwater acoustic source localization .................................... 54

Scope of Future Work ............................................................................................ 54

6 Publications from Thesis Work 55

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List of Figures

Multipath Scenario ........................................................................................... 11

Underwater Communication ........................................................................... 14

BPSK Signal Modulated with Carrier Frequency of 1kHz ....................... 16

Carrier Frequency Offset and its Correction using PLL ........................... 16

Match Filter Output of Noisy Data ............................................................ 17

Received Data Sampled by ADC and its Corresponding Correlation 18

SONAR .......................................................................................................... 19

Hydrophone : ..................................................................................................... 20

Pinger ............................................................................................................................ 20

Long-Baseline (LBL) Systems ........................................................................ 21

Ultra Short Base Line .................................................................................. 22

Short Base Line ............................................................................................. 22

GPS Intelligent Buoys ..................................................................................... 23

Positioning System ........................................................................................... 23

Positioning System with Array of Hydrophone ......................................... 24

Time Delay Estimation ................................................................................ 25

Comparision of Time Delay Estimation between CC and GCC-PHAT 26

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List of Figures

Temperature vs Depth Curve ...................................................................... 29

Sound Velocity vs Depth Curve .................................................................. 29

Sound Velocity Varying with Salanity ..................................................... 30

SL with Varying Range and Frequency........................................................ 30

SL with Varying Range and DT ................................................................ 31

Power with Varying Range and Frequency .................................................. 31

Ambient Noise with Varying Wind Speed and Frequency ..................... 32

Spectrum and Time versions of Coloured Noise .................................... 33

BER Curves for Theoritical and Underwater Simulations using BPSK Modulation ................................................................................................................. 34

BER Curves for Theoritical and Underwater Simulations using FSK Modulation ................................................................................................................. 35

BER Curves for QPSK in AWGN channel and Underwater Channel using QPSK Modulation .......................................................................................... 36

Sensor Arrangement for TDE based Source Localization .................... 38

Performance of CC and GCC-PHAT in Presence of Gaussian Noise 39

Performance GCC-PHAT in Presence of Outlier Data .......................... 40

Multipath Scenario ..................................................................................... 40

CC and GCC-PHAT Performance in Multipath Scenario ...................... 41

Signals Received by Sensor 2 at Different Conditions (i.e signal without any noise, with (noise+outliers) and After Using Order Statistics 45

Performance of GCC-PHAT in Presence of Outliers Before and After Using Proposed Order Statistics (based approach) ................................................45

Performance of GCC-PHAT in Presence of Cauchy Noise Outliers Before and After Using Proposed Order Statistics (based approach) 46

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List of Figures

Performance of GCC-PHAT in presence of cauchy noise a) With- out using Order Statistics b) With Outlier c) With Outlier Using

Proposed Order Statistics (based approach) ................................................ 46

Receiver Operating Charecteristic of the detector devised to compute estimated time delay: With and without Outliers ............................. 47

Receiver Operating Charecteristic of the Detector in without and with Multipath .............................................................................................. 48

Comparison of LRT in presence of Cauchy Noise with and without outlier with diff t amplitude of the signal ............................................ 49

Comparison of Receiver Operating Charecteristic of the Detector a) No multipath b)Multipath c)Multipath with outliers ............................... 50

Probability of Detection of GCC-PHAT and LRT without using Order Statistics in Presence of Outliers .................................................................... 51

Probability of Detection of GCC-PHAT and LRT with using Order Statistics in Presence of Outliers ............................................................................... 51

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Chapter 1 Introduction

Why Underwater

Human being has managed to conquer variety of environments. At some point, humans could walk on the moon, send expeditions to cold or remote areas in diff t corners of the planet. Discovering and exploring new environments is an important human endeavor, a motor for mankinds evolution. One vast environment which is still much unexplored is the underwater world. Robots have potential to help in achieving those discoveries. [1]. Applications 1) Oil and geotechenical exploration. 2) Monitoring of animals. 3) Port and waterway protection. 4) Pollution monitoring in environmental systems. 5) Collection of scientific data recorded at ocean bottom stations. 6) Mapping of the ocean fl or for detection of objects. For exploring these unexplored area by inspecting the sea region by sending the humans to that place is very diffi because there are lots of physical and Envi- ronmental obstacles and also the area is very huge there. To solve these problems we can send some robot attached with cable for communicating, Transmitting and Receiving data from robot, but these cable is very heavy and costly, so we can do wireless communication to solve this problem. Crucial for its successful exploration are reliable communication systems. The topic is complex and there are various diffi in underwater communication such as chemical constitution, environmental variables, and the presence of various types of noise. A promising solution, which has been studied and implemented for communicating within this environ-

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1.2. Related Work

ment, is the use of acoustic waves for the transmission of signals. Electromagnetic waves usually are not considered as a solution for underwater communications because their attenuation is too high. Acoustic waves appear as a good alternative, despite some associated negative aspects. For long communicating distances, an abrupt decay in pressure may occur, impairing the communication quality. This phenomenon may occur even for medium distances and it is dependent of the transmitting acoustic wave frequency. Also in order to accomplish various missions, autonomous underwater vehicles need to be capable of estimating their position within the environment. This is a prerequisite of a successful mission since further tasks that need to be achieved strongly rely on localization information as a source of valuable information. Much has been said about localization in underwater, yet the research has not reached the level of having as equally precise solution for localization as the one available above the water surface. Underwater sound signals classification, localization and tracking of sound sources, are challenging tasks due to the multi-path nature of sound propagation. The system used to acquire the underwater sound signals is based on a set of hydrophones. The hydrophones are usually associated with pre-amplifying blocks followed by data acquisition systems with data logging and advanced signal processing capabilities for sound recognition, underwater sound source localization and motion tracking.

Related Work

The area of UWAC has experienced significant research over the last decades, which led to recent progress in this endeavor. This interest started many years ago when Jean Daniel Colladon, a physicist/engineer, and Charles-Francois Sturn, a mathematician, performed an experiment, back in 1826, which can be thought as the starting point for underwater communications. The experiment took place in the Geneva Lake, in Switzerland, and they used a church bell to prove that sound travels faster in water than in air. One of them lighted a gunpowder fl and at the same time struck the church bell that was underwater. The other started the clock when he saw the gunpowder fl and only stopped it when he heard the noise made by the church bell (to do so he used a trumpet placed underwater as can be seen in fi The distancethat separated the two boats in this experiment was around 10 miles. Despite their simple instruments, they obtained a sound speed in water of 1435 m/s [1]. This measurement was remarkably accurate, considering that the value obtained is not too far from currently known values,

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1.2. Related Work

approximately 1500 m/s [3]. Going further back in time, Leonardo da Vinci, a genius in several fi imagined how one would be able to produce acoustic waves in water and then see what would happen at a distant place, when trying to listen to those waves. Nowadays, fortunately, we have ever-better means and knowledge basis to explore underwater acoustic communications and, not surprisingly, this fi of research is now very active. For underwater channel simulation most of the work at present mostly focus on establishing mathematical model of the underwater acoustic channel. The models of underwater acoustic channel mainly contain the deep vertical channel channel model. Researchers simulate the underwater acoustic channel through establishing mathematical models, and further study the various properties of the acoustic channel by using MATLAB software simulation. And then test this channel with diff t digital modulation schemes.

While in localization the most common approach for passive source localization is to exploit time delay signals received by a pair of sensors. For instance, in sonar signal processing the time delay between signals received by hydrophones is used to estimate the source range and bearings [21], [23], [24]. The basic idea behind time delay estimation is that sensor arrays may be deployed to extract phase information present in signals picked up by spatially separated sensors. When the sensors are spatially separated, the acoustic signals arrive at the sensors with diff in times of arrival. From the known array geometry, Direction of Arrival (DOA) of the signal can be obtained from the measured time delays. The time delays are estimated for each pair of sensors in the array. Finally the best estimate of the DOA is obtained from time delays and geometry [28]. Hence for acoustic source localization, precise time delay estimation is highly essential. There are several methods for time delay estimation like correlation [21], [25], higher order statistics based methods etc [26]. In view of its simplicity, correlation is most commonly adopted method for the purpose. Other time delay estimation methods include -Generalized Cross-Correlation Phase Transform (GCC-PHAT), Maximum Like- lihood (ML) method, Average Square Difference Function (ASDF) method and Least Mean Square (LMS) adaptive fi method. Of all these, GCC-PHAT is the most widely used method to estimate time delay [27]. Two major detterents for underwater source localization are the multipath phenomenon and impulsive noise. These problems lead to false correlation peaks and hence erroneous time delay estimates [29] - [32]. Moreover time delay estimation in passive ranging is in the order of micro-seconds and fl from this lead to inaccurate localization and it is necessary to devise alternative methods which can increase the accuracy of estimated time delay. Since GCC-PHAT is more commonly used method, its

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1.3. Contribution of Thesis Work

performance analysis was taken up and simulation analysis is carried out in underwater ambience. GCC-PHAT works well under the gaussian noise assumption and availability of strong direct path signals. However, if either of these assumptions fail to hold, so does the concept of GCC-PHAT and the estimated time delay. It is felt that very little work is observed to have initiated to improve the capability of GCC-PHAT in presence of multipath and impulsive noise.

Contribution of Thesis Work

(a) Underwater channel noise is simulated in MATLAB: We have simulated underwater channel noise for 8-16 Khz range. (b) Link design for underwater communication: We have calculated power required to transmit a signal for a specified distance, frequency, range and for a fi detection threshold. (c) Testing of different digital modulation schemes on underwater : Dif- ferent digital modulation schemes has been tested in simulated underwater channel. The bit error rate (BER) clearly shows the diff when viewed in an underwater situation it clearly reveals that it requires more SNR than in an AWGN case to transmit data in an underwater case for the same BER. (d) Time delay estimation using GCC-PHAT :GCC-PHAT fails to estimate the time delay in multipath environment and in presence impulsive noise modelled as outlier data and also in presence of Cauchy noise. (e) Performance Improvement of GCC-PHAT using order statistics: It is possible to improve TDE performance of GCC-PHAT in presence of impulsive noise and Cauchy noise by using order statistics. (f) Comparision of GCC-PHAT and signal detection based method for time delay estimation:Signal Detection based TDE is feasible and is much better as compared to traditional GCC-PHAT when SNR at the receiver is low. Moreover, it is also possible to improve the TDE performance of LRT using order-statistics.

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1.4. Thesis Organization

Thesis Organization

The present thesis is divided into six chapters. Brief review of each chapter is enumerated below : (a) Chapter 1: This chapter gives a brief introduction to the idea of the UWA communication and source localization. The motivation behind our attempt at the problem has also been presented and various aspects of its applications have also been discussed. The basic framework of the problem has been mentioned as well. (b) Chapter 2: This chapter gives survey on signal processing for UWA communication and source localization in this channel charecteristics and link design parameter, modelling noise behaviour in shallow water environment, multipath channel, underwater communication and acoustic source localization in underwater ambience. (c) Chapter 3: This chapter comprises a discussion on performance analysis of digital modulation schemes in underwater channel. It includes link design analysis for a UWA communication system, simulation analysis of underwater channel, and analysis of bit error performance of BPSK, FSK, QPSK modem in UW channel. (d) Chapter 4: This chapter presents proposed method to improve UWA source localization method to improve time delay estimation for underwater acoustic source localization. Which include order statistics based approach, signal detection based approach and performance improvement of GCC-PHAT in presence of impulsive noise. (e) Chapter 5: This chapter gives a brief conclusion drawn from the current work on UWA communication and source localization and the scope of the future work.

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Chapter 2 A Survey on Signal Processing for UWA Communication and Source Localization

Underwater Channel

At present, the researches for wireless underwater acoustic (WU-A) channel mostly focus on establishing mathematical model of the underwater acoustic channel. Re- searchers simulate the underwater acoustic channel through establishing mathematical models, and further study the various properties of the acoustic channel by using MATLAB software simulation [2], [3], [4], [5].

Channel Characteristics and Link Design Parameters

Sound propagation under water is primarily determined by transmission loss, noise reverberation, and temporal and spatial variability of the channel. Transmission loss and noise are the principal factors determining the available bandwidth range and SNR Time varying multipath infuences signal design and processing often imposing severe limitations on the system performance. And so for any reliable communication system the fi task should be the LINK DESIGN. Since we are doing underwater communication so we have to design underwater link. This gives what is the power level required with respect to diff t range, frequency, wind speed. For that purpose we fi simulate the underwater link design equation in MATLAB. We have also simulate the noise present in the underwater channel that

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2.1. Underwater Channel

is of colour noise. After that we apply diff t baseband modulation technique and fi the probability of bit error. Acoustic Wave Characteristics : Acoustic waves are a type of longitudinal waves that propagate by means of adiabatic compression and decompression. Lon- gitudinal waves are waves that have the same direction of vibration as their direction of travel. Important quantities for describing acoustic waves are sound pressure, particle velocity, particle displacement and sound intensity. Acoustic waves travel with the speed of sound which depends on the medium theyre pass- ing through [3]- [11] & [43]- [50]. Range, Bandwidth and SNR Transmission loss is caused by energy spreading and sound absorption While the energy spreading loss depends only on the propagation distance, the absorption loss increases not only with range but also with frequency, thus setting the limit on the available bandwidth. In addition to the nominal transmission loss, link condition is largely infuenced by the spatial variability of the underwater acoustic channel Spatial variability is a consequenc of the channel behavior as a waveguide, which results in various phenomena, including formation of the shadow zones. Transmission loss at a particular location can be predicted by many of the propagation modeling techniques with various degrees of accuracy Spatial dependence of transmission loss imposes particularly severe problems for communication with moving sources or receivers. Noise observed in the ocean exhibits strong frequency dependence as well as site dependence. Generally, the inshore environments, such as marinework sites, are much noisier than deep ocean, due to the man made noise Unlike the man made noise, most of the ambientnoise sources can be described as having a continuous spectrum and Gaussian statistics. As a fi approximation, the ambientnoisepower spectral density is commonly assumed to decay at 20 dB/decade, both in shallow and deep water, over frequencies which are of interest to communication systems design Ambient noise, together with frequency dependent transmission loss, determines the relationship between the available range, bandwidth and SNR at the receiver input. This dependence shows the frequency dependent portion of SNR for several transmission ranges. Evidently, this dependence infl the choice of a carrier frequency for the desired transmission range. From the fi the relationship between the available range and frequency band also becomes apparent Underwater acoustic communication links can be classified according to range as very long, long, medium, short and very short links. For a long range system operating over 10-100 km the bandwidth is limited to few kHz for a very long distance on the order of 1000 km the available bandwidth falls below

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a kHz. A medium range system operating over 1-10 km has a bandwidth on the order of 10 kHz while only at very short ranges below about 100m more than a hundred kHz of bandwidth may be available Within this limited bandwidth the signal is subject to multipath propagation through a channel whose characteristics vary with time and are highly dependent on the location of the transmitter and receiver. The multipath structure depends on the link configuration, which is primarily des- ignated as vertical or horizontal While vertical channels exhibit little multipath, horizontal channels may have extremely long multipath spreads. Most notable in the long and medium range channels, multipath propagation causes severe degrada- tion of the acoustic communication signals. Combating the underwater multipath to achieve a high data throughput is without exception considered to be the most challenging task of an underwater acoustic communication system [1]. (a)Acoustic pressure : Given a plane wave, acoustic Pressure (P), with unit Pa or N/m2, is defi by the following equation

v p = 0 = c2f, = 2f (2.1)

where 0 represents the fl density, c is the velocity of the sound wave propagation

and v is the particle velocity. The variable v is equivalent to 2pf . This quantity P is analogous to the potential diff inelectrical circuits.The quantity 0 c is

called specific impedance and has the same role has the intrinsic impedance defi for a transverse electromagnetic. (b) Acoustic impedance :The acoustic impedance is given by

P

Z = (2.2) U

where U is the acoustic volume fl w. This equation is analogous to Ohms law and Z is a function offrequency, with real and imaginary components. (c)Acoustic intensity : The acoustic intensity I (unit/m2) is the energy per second that crosses the unit area. For a planewave it is given by:

I = P v (2.3)

so that it may be viewed as the acoustic power density produced by the source. Normally, a reference intensityIr is defi ed for each medium under certain circum- stances. For example, the underwater reference intensity is the one produced by a plane wave with root mean square pressure of1 Pa.

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(d) Sound speed profiles : Sound speed in water depends of several parameters, such as temperature,salinity and pressure.

c(T, S, Z) = a1 +a2T +a3T 2 +a4T 3 +a5(S35)+a6Z +a7Z2 +a8T (S35)+a9T Z3

(2.4) Where T, S, and z are temperature in degrees Celsius, salinity in parts perthousand and depth in meters, respectively. The constants a1, a2,...., a9 are a1 = 1448.96, a2 = 4.591, a3 = 5.304 102 , a4 = 2.3746 104 , a5 = 1.340 , a6 = 1.630 102 a7 = 1.675 107, a8 = 1.025 102, a1 = 7.139 1013 (e) Directivity Index:The directivity index (DI) may be defi as the ratio of the intensity of a source in some specified direction (usually along the acoustic axis of the source) to the intensity at the same point in space of an Omni - directional point source with the same acoustic power. Through the principle of reciprocity, the same principle applies to the receiving transducer. The transmitter and receiver directivity are analogous to the RF terms for antenna gain. (f) Acoustic Source Level : The source level quantity associated with a projector, is commonly defi in terms of the sound pressure level at a well-defined distance of 1m from its acoustic center. The source intensity at this reference point is:

I = Ptx

Area (2.5)

SLprojector = 10log((Ptx/12.6)/Iref )(dB) (2.6)

SLprojector (P, , DI) = 170.8 + 10logPtx + 10logtx + DItx(dB) (2.7)

PSL = SL 10log10(W ) (2.8)

(g) Transmission Loss: Transmission loss is the reduction in signal intensity which occurs as an acoustic wave propagates away from transmission source. Trans- mission loss in ocean can be categorized according to spreading loss and attenuation loss. Absorption loss describes those effects in the ocean in which a portion of the sound intensity is lost through convention of the heat.

(f ) =

0.11f 2 1 + f 2

44f 2 +

4100 + f 2 + 2.75 104f 2 + 0.003 (2.9)

SS = 20logr

TL = SS + r 103 where f is frequency in KHz, r is the range in meter, SS is a spherical spreading factor and is absorption coefficient . Spreading loss consists of cylindrical spreading and spherical spreading. Cylindrical spreading usually occurs in shallow water

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and spherical spreading in deep water. (h) Noise : The ambient noise in underwater environment can be divided into 4 major factors noise: Turbulence (Nt) , Shipping (Ns), Wind (Nw ) and Thermal (Nth). Figure 4.5 shows the variation of noise with frequency at diff t wind speeds.

10logNt(f ) = 17 30logf 10logNs(f ) = 40 + 20(s 0.5) + 26logf 60log(f + 0.03)

1 10logNw (f ) = 50 + 7.5w 2 + 20logf 40log(f + 0.4)

10logNth(f ) = 15 + 20logf

(2.10)

(i) Received SNR : The acoustic link uses basic sonar theory to estimate the available signal level at the receiver.

SNR = PSL TL AN + DIxmt + DIrev (2.11)

where SNR = signal to noise ratio at the receiver. PSL = pressure spectrum level of transmitting platform TL = transmission loss in medium AN = ambient noise DIxmt = transmitter directivity DIrev = receiver directivity All quantities are expressed in dB re 1Pa

Modelling Noise Behaviour in Shallow Water Envi- ronment

A shallow water communication channel [5] is a combination of several spiky signals. It is for this reason, a underwater channel is commonly modeled to behave impulsive in nature. In general impulsive noise is modeled in two ways: a) Heavy tailed non gaussian distributed noise ( - stable). b) Contaminated gaussian noise (i.e. gaussian noise with outlier data). High velocity-water leading to the formation of a cavitation bubble, which colapses rapidly, causing aloud broadband snapping sound. The shrimp are usually found in such large numbers that there is a permanent crackling background noise in

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warm shallow waters throughout the world. As ambient snapping shrimp noise is composed of impulsive noise sources, the resulting noise statistics are non gaussian.

Several publications report that underwater acoustic noise (UWAN) does not follow the normal distribution. In fact, this type of noise shows probability density function with extended tails shape, reflecting an accentuated impulsive behavior due to the high incidence of large amplitude noise events. From these sources, it

is known that UWAN follows the alpha-stable distribution class [4]. An symmetrical alpha-stable (SS) distribution has a characteristic function given by.

() = E[exp(jX)] = exp(j ||) (2.12)

where is the location parameter and > 0 is the dispersion parameter. Cauchy and Gaussian distributions are particular cases of the alpha-stable distribution for = 1 and = 2 respectively. The cauchy noise contains powerful noise spikes that can be more than a hundred times the magnitude of the humbler gaussian noise spikes. The thicker tails on the cauchy bell curve mean that extreme events have more probability of occuring then they do with the thinner-tailed gaussian curve.

Multipath Phenomenon in Underwater Channel

While wireless communication technology today has become part of our daily life, the idea of wireless undersea communications may still seem far-fetched. However, research has been active for over a decade on designing the methods for wireless information transmission underwater.

Human knowledge and understanding of the worlds oceans, which constitute

Figure 2.1: Multipath Scenario

the major part of our planet, rests on our ability to collect information from remote undersea locations. The major discoveries of the past decades, such as the

11


remains of Titanic, or the hydro-thermal vents at bottom of deep ocean, were made using cabled submersibles. Although such systems remain indispensable if highspeed communication link is to exists between the remote end and the surface, it is natural to wonder what one could accomplish without the burden (and cost) of heavy cables. Hence the motivation, and our interest in wireless underwater communications. Together with sensor technology and vehicular technology, wireless communications will enable new applications ranging from environmental monitoring to gathering of oceanographic data, marine archaeology, and search and rescue missions. The signals that are used to carry digital information through an underwater channel are not radio signals, as electro-magnetic waves propagate only over extremely short distances. Instead, acoustic waves are used, which can propagate over long distances. However, an underwater acoustic channel presents a communication system designer with many diffi The three distinguishing characteristics of this channel are frequency-dependent propagation loss, severe multipath, and low speed of sound propagation. None of these characteristics are nearly as pronounced in land-based radio channels, the fact that makes underwater wireless communication extremely diffi t, and necessitates dedicated system design. Path loss that occurs in an acoustic channel at distance d limits the available bandwidth: for example, at distances on the order of 100 km, the available bandwidth is only on the order of 1 kHz. At shorter distances, a larger bandwidth is available, but in practice it is limited by the that of the transducer. Also in contrast to the radio systems, an acoustic signal is rarely narrowband, i.e., its bandwidth is not neg- ligible with respect to the center frequency. Within this limited bandwidth, the signal is subject to multipath propagation, which is particularly pronounced on horizontal channels. In shallow water, multipath occurs due to signal reflection from the surface and bottom. In deep water, it occurs due to ray bending, i.e. the tendency of acoustic waves to travel along the axis of lowest sound speed. The multipath spread, measured along the delay axis, is on the order of 10 ms in this example. The channel response varies in time, and also changes if the receiver moves. Regardless of its origin, multipath propagation creates signal echoes, resulting in intersymbol interference in a digital communication system. In a digital communication system which uses a single carrier multipath propagation causes intersymbol interference (ISI) and an important fi of merit is multipath spread in terms of symbol intervals While typical multipath spreads in the commonly used radio channels are on the order of several symbol intervals in the horizontal underwater acoustic channels they increase to several tens, or a hundred of symbol intervals

12


for moderate to high data rates For example a commonly encountered multipath spread of 10 ms in a medium range shallowwater channel causes the ISI to extend over 100 symbols if the system is operating at a rate of 10 kilosymbols per second (ksps). The mechanisms of multipath formation in the ocean are diff t in deep and shallowwater and also depend on the frequency and range of transmission. Understanding of these mechanisms is based on the theory and models of sound propagation. Depending on the system location there are several typical ways of multipath propagation [2], [3], [4], [36]. It is mostly the water depth that determines the type of propagation. The defi of shallow and deep water is not a strict one, but usually implies the region of continental shelves, with depth less than about 100 m, and the region past the continental shelves, respectively. Two fundamental mechanisms of multipath formation are reflection at the boundaries (bottom, surface and any objects in the water), and ray bending (sound speed is a function of depth) and the rays sound always bend towards regions of lower propagation speed If the water is shallow, such as in the littoral region and the region of continental shelves, propagation will occur in surface bottom bounces in addition to a possible direct path If the water is deep, as in the regions past the continental shelves, the sound channel may form by bending of the rays toward the location where the sound speed reaches its minimum, called the axis of the deep sound channel. Since there is no loss due to reflections, sound can travel in this wayover several thousands of kilometers. Alternatively, the rays bending upwards may reach the surface focusing in one point where they are reflected, and the pro- cess is repeated periodically. The region between two focusing points on the surface is called a convergence zone, and its typical length is 60-100 km. The geometry of multipath propagation and its spatial dependence are important for communication systems which use array processing to suppress multipath The design of such systems is accompanied by the use of a propagation model for predicting the multipath configuration. Ray theory and the theory of normal modes provide basis for such propagation modeling. Recent references commonly use ray tracing for determining the coarse multipath structure for communication channel modeling. A diff t class of underwater acoustic communication systems has recently been developed which do not rely on the particular multipath geometry, and is equally applicable in a varietyofchannels, regardless of the parameters such as range to depth ratio which determine the angles of incidence of multipath arrivals [1] [6]. As we have seen Shallow water channels being multipath dominated, signals travelling through acoustic channel suffer from multipath induced fading effects. Such multipath phenomenon leads to constructive as well as destructive interference and

13

2.2. A Brief note on Underwater Acoustic Communication

results in phase shift of the signal.

2.2 A Brief note on Underwater Acoustic Com- munication

Figure 2.2: Underwater Communication

Figure 2.2 shows typical underwater acoustic communication. Underwater acoustic communication is a technique of sending and receiving message below water [7], [19], [36], [37]. There are several ways of employing such communication but the most common is using hydrophones. Under water communication is diffi due to factors like multi-path propagation, time variations of the channel, small available bandwidth and strong signal attenuation, especially over long ranges. In underwater communication there are low data rates compared to terrestrial communication, since underwater communication uses acoustic waves instead of electromagnetic waves.

14


2.2.1 Coherent Modulation

With the goal of increasing the bandwidth effciency of an underwater acoustic communication system research focus over the past years has shifted towards phase-coherent modulation techniques, such as PSK phase shift keying and QAM quadrature amplitude modulation. Phase-coherent communication methods, pre- viously not considered feasible, were demonstrated to be a viable way of achieving highspeed data transmission over many of the underwater channels, including the severely time spread horizontal shallow water channels. The new generation of underwater acoustic communication systems, based on the principles of phase coherent detection techniques, is capable of achieving raw data throughputs that are an order of magnitude higher than those of the existing noncoherent systems.

Depending on the method for carrier synchronization, phase-coherent systems fall into two categories - diff tially coherent and purely phase-coherent. The advantage of using diff tially encoded PSK (DPSK) with diff tially coherent detection is the simple carrier recovery it allows. Its disadvantage is performance loss as compared to coherent detection. While bandwidth efficient methods have successfully been tested on a variety of channels, real time systems have mostly been implemented for application in vertical and the very short range channels, where little multipath is observed and the phase stability is good. In the very short range channel, where bandwidth in excess of 100 kHz is available, are representa- tive system operates over 60 m at a carrier frequency of 1MHz and a data rate of 500 kbps. This system is used for communication with an undersea robot which performs maintenance of a submerged platform. BPSK Modulation and Demodulation : BPSK or Binary Phase Shift Keying is a simple modulaton scheme where a phase change of represents a transition of a 1 to 0 or vice versa. Each bit here represents a symbol. Modulation is first carried out in baseband. This is simply done by changing 0s to -1. Then passband transformation is obtained by multiplying a carrier signal with a carrier with desired frequency. Figure 2.3 shows a sample of BPSK signal modualted with carrier frequency of 1kHz. The modulated signal is then sent through channel where it gets added to noise. At the receiever the noise added signal is received and demodulation is carried out to retransform the passband signal to corresponding baseband form. However demodulation requires the generation of carrier with exact frequency and also the knowledge of exact symbol time. Therefore schemes are employed to generate a local oscillator signal whose frequency offset is corrected through a PLL and exact symbol time is obtained by an early-late gate scheme.

15


Figure 2.3: BPSK Signal Modulated with Carrier Frequency of 1kHz

Carrier Frequency Off : CFO or Carrier frequency offset arises due to im- perfect local oscillator signal generation. Even a very highly accurate oscillator has some offset and to correct it a phased locked loop design is essential. The phase locked loop here is a proportional and integral controller which makes it a second order system. Figure 2.4 shows the effect of offset in carrier and how the PLL corrects the offset.

Symbol Timing Recovery : At the receiver the samples of signal received

Figure 2.4: Carrier Frequency Offset and its Correction using PLL

is sampled by ADC. ADC samples at a high rate and after sampling the number of samples to be taken as a symbol has to be determined. Essentially this is the

16

2.3. Acoustic Source Localization in Underwater Ambience

same as determining the correct sampling instant. The function of ADC can be reciprocated as that of an interpolator in simulation. The timing estimate is done fi by a match fi whose impulse response is basically same as that of the signal. Thus the convolution is same as autocorrelation of the signal which gives a rough estimate of the symbol time. To correctly determine the symbol time, an early-late gate scheme is used. However this scheme essentially requires at least 3 samples per symbol and is only used for low data rate purposes. Figure 2.5 shows the match fi output of noisy data for 3 symbols having 40 samples each. Figure 2.16 shows the data sampled by ADC and its corresponding match fi output.

Figure 2.5: Match Filter Output of Noisy Data

2.3 Acoustic Source Localization in Underwater Ambience

Recent years have witnessed an impressive growth in the technology of robotics for undersea exploration. And autonomous underwater vehicles (AUVs) are still more research topics than commercial products; however, they held the promise of being

17


Figure 2.6: Received Data Sampled by ADC and its Corresponding Correlation

the next significative step in ocean exploration and exploitation, cutting costs and allowing operations that are presently prohibitive from surface ships or by ROVs. One of the problems that prevents commercial applications of AUVs, or at least mitigate their efficiency, is that of vehicle localization: on-board systems, as in- ertial navigation systems (INS), cannot maintain the requested accuracy over the desired interval of operation of the system,and are highly expensive. Localization in underwater is challenging as radio frequency (RF) waves are heavily attenuated under water and hence, employing technology like GPS is not feasible. A number of localization schemes have been proposed to date which take into account a number of factors like the network topology, device capabilities, signal propagation models and energy requirements. Most localization schemes require the location of some nodes in the network to be known. Nodes whose locations are known are referred to as anchor nodes or reference nodes in the literature. The localization schemes that use reference nodes can be broadly classified into two categories: range-based schemes (schemes that use range or bearing information), and range-free schemes (schemes that do not use range or bearing information). Here our main emphasis is on range-based schemes (schemes that use range or bearing information). Range-Based Schemes :In range-based schemes, precise distance or angle measurements are made to estimate the location of nodes in the network. Range- based schemes, which rely on range and/or bearing information, use time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA) or received signal strength indicator (RSSI) to estimate their distances to other nodes in the system. UWB-based localization , GPS , and Cricket are examples of schemes that

18


use ToA or TDoA of acoustic or RF signals for localization in terrestrial sensor networks. Techniques used for underwater acoustic source localization SONAR: SONAR (originally an acronym for Sound Navigation And Ranging)

Figure 2.7: SONAR

is a technique that uses sound propagation (usually underwater, as in submarine navigation) to navigate, communicate with or detect objects on or under the surface of the water, such as other vessels. Two types of technology share the name SONAR. Passive SONAR is essentially listening for the sound made by vessels. Active SONAR is emitting pulses of sounds and listening for echoes. SONAR as shown in fi 2.7 may be used as a means of acoustic location and of measurement of the echo characteristics of targets in the water. Acoustic location in air was used before the introduction of radar. SONAR may also be used in air for robot navigation. The term SONAR is also used for the equipment used to generate and receieve the sound. The acoustic frequencies used in SONAR systems vary from very low (infrasonic) to extremely high (ultrasonic). The study of underwater sound is known as underwater acoustics or hydroacoustic. Hydrophone : Figure 2.8 shows hydrophone (Greek hydro = water and phone = sound) is a microphone designed to be used underwater for recording or listening to underwater sound. Most hydrophones are based on a piezoelectric transducer that generates electricity when subjected to a pressure change. Such piezoelectric materials, or transducers can convert a sound signal into an electri- cal signal since sound is a pressure wave. Some transducers can also serve as a projector, but not all have this capability, and may be destroyed if used in such

19


Figure 2.8: Hydrophone :

a manner. A hydrophone can listen to sound in air, but will be less sensitive due to its design as having a good acoustic impedance match to water, which is a denser substance than air. Likewise, a microphone can be buried in the ground, or immersed in water if it is put in a waterproof container, but will give similarly poor performance due to the similarly bad acoustic impedance match. Pinger : Pingers as shown in fi 2.9 are guiding devices used to provide an

Figure 2.9: Pinger

acoustic energy source that can be heard with a hydrophone. Used as an acoustic beacon, pingers are commonly deployed for the relocation of equipment or revis- itation by divers to a specific site. Pingers can also be attached to underwater vehicles for tracking and navigation purposes. The pinger is attached to an underwater location where it continuously transmits a sonar signal [12]- [19]. Underwater Acoustic Positioning System: An underwater acoustic positioning system is a system for the tracking and navigation of underwater vehicles or divers by means of acoustic distance and/or direction measurements, and subse- quent position triangulation. Underwater acoustic positioning systems are commonly used in a wide variety of underwater work, including oil and gas exploration,

20


ocean sciences, salvage operations, marine archaeology, law enforcement and mili- tary activities. Underwater acoustic positioning systems are generally categorized into three broad types or classes. (a) Long-Baseline (LBL) systems :Long-baseline (LBL) systems, as shown in fi 2.10 below, use a sea-floor baseline transponder network. The transponders

Figure 2.10: Long-Baseline (LBL) Systems

are typically mounted in the corners of the operations site. LBL systems yield very high accuracy of generally better than 1 m and sometimes as good as 0.01m along with very robust positions. This is due to the fact that the transponders are installed in the reference frame of the work site itself (i.e. on the sea fl or), the wide transponder spacing results in an ideal geometry for position computations, and the LBL system operates without an acoustic path to the (potentially distant) sea surface. (b) USBL - Ultra Short Base Line :As we see in fi 2.11 The calculation of positioning is based on range and on vertical and horizontal angle measurements, from a single multi element transducer. The system provides three-dimensional transponder positions relative to the vessel. The HPR (Hydroacoustic Position- ing Reference) and HiPAP (High Precision Acoustic Positioning) systems are both leaders within these principles.The disadvantage is that positioning accuracy and robustness is not as good as for LBL systems.

21


Figure 2.11: Ultra Short Base Line

(c) SBL- Short Base Line : As we see in fi 2.12 SBL. The calculation of

Figure 2.12: Short Base Line

position is based on range, and vertical and horizontal angle measurements from a minimum of three hull mounted transducers. The baselines are between transducers on the vessel. A transponder is positioned relative to the vessel. Accuracy is

22


0.5 %of slant range. The system provides three-dimensional transponder positions relative to the vessel. (d) GPS intelligent buoys (GIB) : As we see in fi 2.13 GPS intelligent

Figure 2.13: GPS Intelligent Buoys

buoys (GIB) systems are inverted LBL devices where the transducers are replaced by fl buoys, self-positioned by GPS. The tracked position is calculated in realtime at the surface from the Time-Of-Arrival (TOAs) of the acoustic signals sent by the underwater device, and acquired by the buoys. Such configuration allow fast, calibration-free deployment with an accuracy similar to LBL systems. At the opposite of LBL, SBL ou USBL systems, GIB systems use one-way acoustic signals from the emitter to the buoys, making it less sensible to surface or wall reflections. GIB systems are used to track AUVs, torpedoes, or divers. Hydroacoustic positioning principles : A hydroacoustic positioning as shown

Figure 2.14: Positioning System

in fi 2.14 & 2.15 system consists of both a transmitter (transducer) and a receiver (transponder). A signal (pulse) is sent from the transducer, and is aimed

23


towards the seabed transponder. This pulse activates the transponder, which re- sponds immediately to the vessel transducer. The transducer, with corresponding electronics, calculates an accurate position of the transponder relative to the base station [8] [9] [10].

Time delay Estimation for Acoustic Source Localization: There are many

Figure 2.15: Positioning System with Array of Hydrophone

techniques to estimate the position of a sound source based on energy densities, intensity of received signals, triangulation, particle velocity etc [22]. However, the most common approach for passive source localization is to exploit time delay signals received by a pair of sensors. For instance, in sonar signal processing the time delay between signals received by hydrophones is used to estimate the source range and bearings [24]. The basic idea behind time delay estimation is that sensor arrays may be deployed to extract phase information present in signals picked up by spatially separated sensors. When the sensors are spatially separated, the acoustic signals arrive at the sensors with diff in times of arrival. From the known array geometry, Direction of Arrival (DOA) of the signal can be obtained from the measured time delays. The time delays are estimated for each pair of sensors in the array. Finally the best estimate of the DOA is obtained from time delays and geometry [28], [41]- [50]. Hence for acoustic source localization, precise time delay estimation is highly essential. Here we have simulated two Time Delay Estimation technique(CC and GCC- PHAT) in matlab and compaired both the technique and found that GCC-PHAT is the best technique it is more accurate than CC Two methods of TDE estimation are presented, they are as follows.

24


Figure 2.16: Time Delay Estimation

(a) Cross Correlation: In signal processing cross correlation is a measure of similarity between 2 signals as a functionof time lag between them. One common method to estimate the time delay is to compute the cross correlation function between the received signals at two microphones. Then locate the maximum peak in the output which represents the estimated time delay . The CC can be modelled by: Let there be 2 signals:

S1(t) = X1(t) + n1(t). (2.13)

S2(t) = X2(t) + n2(t) (2.14)

For continuous signals S1 (t) and S2(t) cross correlation is defi as

[S1 S2] = r

S1( )S2( + t)d( ) (2.15)

where * is the complex conjugate of a function TDOA is given as follows

TDOA = argmax([S1 S2](t)) (2.16)

As the signals we will be using are sampled discrete signals cross correlation for discrete signals is defi as

(S1 S2)[n] = k= )

k= S1[k]S2[n + k] (2.17)

TDOA = argmax((S1 S2)[n])/Fs (2.18)

25


| | co

rrela

tion

peak

where Fs is defi as the sampling rate. (b) GCC-PHAT (Generalized Cross-Correlation using Phase transform) : A way to sharpen the cross correlation peak is to whiten the input signals by using weighting function, which leads to the so-called generalized cross-correlation technique (GCC). The block diagram of a generalized cross-correlation processor is shown in Figure . The PHAT is a GCC procedure which has received considerable attention due to its ability to avoid causing spreading of the peak of the correlation function ]. This can be expressed mathematically by:

X1(f ) = F (S1(t)) (2.19)

X2(f ) = F (S2(t)) (2.20)

where F is the Fourier transform of a function

X1(f ) X2(f ) GP HAT = |X1(f )| |X2(f )|

(2.21)

TDOA = argmax(Rphat(t)) (2.22)

and Rphat (t) is defi as:

Rphat = F1(GP HAT ) (2.23)

There are several other methods for time delay estimation like correlation [11]

2

1.5

1

0.5

0

0.5

5 x 10

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

1 0 2000 4000 6000 8000 10000 12000 14000 16000 18000

correlation lag

0

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 correlation lag

4 x 10

15

10

CC 0.7

0.6

0.5

GCCPHAT

0.4

5 0.3

0 0.2

5 200 210 220 230 240 250 260 270 280 290

correlation lag

0.1

200 210 220 230 240 250 260 270 280 290 correlation lag

Figure 2.17: Comparision of Time Delay Estimation between CC and GCC-PHAT

corre

latio

n pe

ak

corre

latio

n pe

ak

corre

latio

n pe

ak

26

2.4. Conclusion

[12] [21] [25], [26] higher order statistics based methods etc. In view of its simplicity, correlation is most commonly adopted method for the purpose. Other time delay estimation methods include, Maximum Likelihood (ML) method, Av- erage Square Difference Function (ASDF) method and Least Mean Square (LMS) adaptive fi method. Of all these, GCC-PHAT is the most widely used method to estimate time delay [27].

2.4 Conclusion

From this chapter we can acknowledge that for underwater channel simulation mathematical model have been established which is complex, there is a need of simple method. And in underwater localization lot has been said about time delay estimation using correlation techinique but it may fail in underwater ambience because of impulsive and multipath behaviour, There is need to improve the time delay estimation method.

27

Chapter 3 Performance Analysis of Digital Modulation Schemes in UW Channel

In chapter-2 we have seen link design parameters and channel charecteristics and about some digital modulation schemes which is very important for any communication system. Link design gives what is the power level required with respect to diff t range, frequency, wind speed. So for acheiving the above task in this chapter we fi simulate the underwater link design equation, simulated the noise present in the underwater channel that is of colour noise and subsequently we have applied diff t digital baseband modulation technique to fi the probability of bit error such as BPSK, FSK and QPSK in MATLAB.

3.1 Link Design Analysis for a UWA Communi- cation System

Figure 3.1 shows the typical temperature profi with surface of the sea at higher temperature than the temperature at the sea bed. Here we can see, temperature decreases with depth till some depth value 300 m and after that getting constant. This corresponds to a summer profi of a typical sea. The impact of temperature and pressure upon the sound velocity, c is shown in Figure . This can be viewed in three domains. In the fi domain, temperature is the dominating factor upon the velocity of sound. In the second domain or transition domain, both the temperature and depths are dominating upon the velocity of sound. In the third domain,

28

3.1. Link Design Analysis for a UWA Communication System

Figure 3.1: Temperature vs Depth Curve

sound velocity purely depends on depths. These three domains can be seen in fi 3.2, fi domain is till depths of 200 m,

Figure 3.2: Sound Velocity vs Depth Curve

transition domain is from 200-400 m and the third domain is above 400m. Depen- dence of c on salinity, S is shown in fi 3.3. Here, with the increase of S,velocity of sound, c, also increases keeping the shape of the profi unaff [20]. From fi 3.4 we can see that for a fi frequency if we increase the range, source level requred is also increasing so as we increase the range source level required is also increasing and from fi we can also see that for fi range as we increase the frequency, source level required is also high. From figure 3.5 we can see that for a fi detection threshold if we increase the range, source level requred is also increasing so as we increase the range source level required is also increasing and from fi we can also see that for fi range as we increase the detection threshold , source level required is also increasing. From fi 3.6 we can see that

29


Figure 3.3: Sound Velocity Varying with Salanity

f 120

110

100

90

80

70

60 0 200 400 600 800 1000 1200 1400 1600 1800 2000

range in meter

Figure 3.4: SL with Varying Range and Frequency

for a fi frequency if we increase the range, power requred to transmit the signal is also increasing so as we increase the range power required is also increasing and from fi we can also see that for fi range as we increase the detection threshold power required is also high. From fi 3.7 below we can see that ambient noise is frequency and wind speed dependent as we increase the frequency for a fi wind speed ambient noise decreases and after 100 hz for a fi frequncy as wind speed increases ambient noise increase. In this chapter we present the results obtained from a BPSK system with frequency offset correction and symbol timing recovery in underwater scenario. The description for simulating underwater noise

4KHZ 8KHZ 12KHZ 16KHZ

SL

in d

B

30


10

140

130

120

110

100

90

80

70

60 0 200 400 600 800 1000 1200 1400 1600 1800 2000

range in meter

Figure 3.5: SL with Varying Range and DT

x 10 Transmit Power vs Range: For a given Freq, DT = 20dB

3.5

3

F = 4kHz F = 8kHz F = 12kHz F = 16kHz

2.5

2

1.5

1

0.5

0

0 500 1000 1500 2000 range in meter

Figure 3.6: Power with Varying Range and Frequency

is presented and the performance of BPSK modulation is descibed in the following subsections.

Simulating Underwater Channel Communication channel modeling is important in detection theory. Due to the randomness of the underwater medium, it is imperative to conduct a statistical approximation of the real environment of the sea. Itis particularly diffi to generate an exact statistical representation of the underwater channel due to its inhomogeneity and non-stationary. It is common in the scientific and engineer-

SL vs range with DT = 20 dB SL vs range with DT = 0 dB

Pow

er in

mic

ro W

atts

S

Lin

dB

31


Figure 3.7: Ambient Noise with Varying Wind Speed and Frequency

ing world to use additive white Gaussian noise (AWGN) to represent the noise in communication channels. Although it provides researchers with fairly good approximation of the real data, it does not address specific cases of noise power distribution. In this research, we will use the power spectral density function to simulate model of underwater noise.

As highlighted before, the noise for an underwater scenario depends on frequency and wind speed. For simulation purpose a frequency range of 8-16 kHz has been chosen. Colour noise is modeled by fi additive white noise with underwater ambient noise spectrum level profi for wind speed 4 to 6 knots. For this the ambient noise spectrum level values are obtained from Wenz curves (Figure3.7) for sea state-3 at 1khz discrete intervals from 8khz to 16khz . In order to simulate the underwater ambient noise scenario, first Additive White Gaussian Noise(AWGN) with zero mean is generated. A FFT is applied to this AWGN noise to obtain its frequency response. Then a point-to-point multiplication of the ambient noise spectrum values and the AWGN noise at 1 khz interval is carried in frequency domain. These frequency domain values are again subjected to IFFT to get the time domain version of the noise. The spectrum and time domain versions of the simulated colour noise are shown in Figure 3.8.

32


10

x 4 Spectrum of coloured noise 5

4

3

2

1

0 2 1.5 1 0.5 0 0.5 1 1.5 2

f(Hz) 4 x 10

Coloured Noise 4

2

0

2

4

6 0 0.5 1 1.5 2 2.5 3 3.5 4

4 x 10

Figure 3.8: Spectrum and Time versions of Coloured Noise

Bit Error Performance of BPSK Modem in UW Chan- nel

For simualtion purpose random data has been generated and is BPSK modulated with a carrier of frequency 12 kHz [37]- [40]. After modulation the generated coloured noise is added and then demodulation has been performed with carrier offset correction and timing recovery schemes thus recreating a complete BPSK system. The performance metric used is the bit error rate (BER) calculation. The theoritical and the observed values are plotted to obtain Figure. From fi 3.11 we can see BPSK BER in presence of white, color noise with and without carrier offset. Here we can see that BER is highest for color noise with offset for a fi SNR. This clearly shows that the it is very diffi to do underwater communication as compaired to doing communication in free space.

Am

plitu

de

|N

(f)

|

t

33


Bit error probability curve for bpsk 0

10

1 10

2 10

3 10

4 theory 10

white noise colour noise white noise with offset

5 colour noise with offset 10

white noise with offset corrected colour noise with offset corrected

6 10

7 10

6 4 2 0 2 4 6 8 10 12 snr(dB)

Figure 3.9: BER Curves for Theoritical and Underwater Simulations using BPSK Mod- ulation

Bit Error Performance of FSK Modem in UW Chan-

nel

Like BPSK here also for simulation purpose random data has been generated and is FSK modulated with a carrier of frequency 12 kHz. After modulation the generated coloured noise is added and then demodulation has been performed with carrier offset correction and timing recovery schemes thus recreating a complete FSK system. The performance metric used is the bit error rate (BER) calculation. The theoritical and the observed values are plotted to obtain Figure. From fi 3.10 we can see FSK BER in presence of white, color noise with and without carrier offset. Here we can see that BER is highest for color noise with offset for a fi SNR. This clearly shows that the it is very diffi to do underwater communication as compaired to doing communication in free space.

Bit

Err

or

Ra

te

34


Bit error probability curve for FSK with coloured noise 0

10

1 10

2 10

3 10

4 10

0 1 2 3 4 5 6 7 8 9 10 snr(dB)

Figure 3.10: BER Curves for Theoritical and Underwater Simulations using FSK Mod- ulation

3.1.3 Bit Error Performance of QPSK Modem in UW Chan-

nel

For simualtion purpose random data has been generated and is QPSK modulated with a carrier of frequency 12 kHz. After modulation the generated coloured noise is added and then demodulation has been performed thus recreating a complete QPSK system. The performance metric used is the bit error rate (BER) calculation. The BER performance in presence of AWGN and Colored noise channel observed values are plotted to obtain fi rom fi 3.11 we can see QPSK BER in presence of white, color noise . Here we can see that BER is highest for color noise for a fi SNR.

theoretical ber ber with coloured noise and with offset ber with coloured noise and without offset ber with coloured noise and with offset correction

Bit

Err

or

Ra

te

35

3.2. Conclusion

bit error probabality vs snr 0

10

1 10

2 10

3 10

4 10

5 10

6 10

10 8 6 4 2 0 2 4 6 8 10 snr in dB

Figure 3.11: BER Curves for QPSK in AWGN channel and Underwater Channel using QPSK Modulation

Conclusion

So from this chapter results we can analyze that bit error performance of diff t digital modulation schemes performes badly in underwater channel as compared to AWGN channel. So from above results we can see that receiving correct message is extremly difficult in underwater scenario. It require more power and with many other limitation such as low bandwidth, low data rate, low range etc.

QPSK BER in Presence of AWGN Channel QPSK BER in Presence of Color Noise

pro

ba

ba

lity

of

bit

err

or

36

1

Chapter 4 Proposed Method to Improve UWA Source Localization

In chapter-3 we have seen the BER performance of diff t digital modulation schemes. In this chapter we try to analyze the performance of well known time delay estimation ( TDE ) techinique (GCC-PHAT) in underwater scenario where noise is of impulsive nature and presence of multipath environment. And we have proposed improved TDE method for underwater scenario.

Let s(t) represent a signal source periodically transmitting a signal. Let x1(t) and x2(t) be signals received by two sensors at a distant location, arranged in a known geometry. The received continous time signal is converted into discrete time signal by an ADC as x1[n] and x2[n]. We then have,

x1[n] = s[n] + v1[n]

x2[n] = s[n D] + v2[n] (4.1)

where v1[n] and v2[n] represent additive white gaussian noise and D is the discrete time delay after which the second sensor received the transmitted signal. In general, cross correlation of discrete time signals is defi as :

Rx1x2 [n] =

k= )

k= x[k]x2[n + k] (4.2)

The Time Difference of Arrival (TDOA) of the two signals at the receivers is given as,

TDOA = argmax(Rx1x2 [n])/Fs (4.3)

37

2

2

Figure 4.1: Sensor Arrangement for TDE based Source Localization

where Fs is the sampling rate [31]. In case of low SNR at the receiver CC usually fails to predict accurate time delay due to multiple correlation peaks. Generalized Cross Correlation (GCC) is a modifi version of CC commonly used to estimate time delay, D. Phase Transform (PHAT) is a modifi version of GCC which has received considerable attention due to its ability to avoid spreading of correlation peak [21]. GCC-PHAT can be mathematically expressed as:

GP HAT = X1(f ) X(f ) |X1(f )| |X(f )|

RP HAT (n) = F1(GPHAT ) (4.4)

The Time Difference of Arrival (TDOA) is then obtained as,

TDOA = argmax {RP HAT (n)} (4.5)

Once time delay D is computed, one may also compute direction of arrival of signal as :

= sin1

cD

d

(4.6)

where d is the distance between two sensors, D is the estimated time delay, is the angle of arrival of signal and c is the speed of sound in water. Since signal and noise are uncorrelated (Rxv (n) = 0) Rx1x2 (n) has a correlation peak at n = D [26].

Figure 4.2 shows performance of TDE CC and GCC-PHAT in the presence of white gaussian noise. A sharp peak corresponding to the time delay (D = 70) may

be seen in case of GCC-PHAT which indicates its improvement over CC. In chapter-2 it is described that a shallow water communication channel is a

combination of several spiky signals [31] and also shallow water channels being multipath dominated . We evaluate the performance of GCC-PHAT in presence of

38

Comparison of CC and GCC in AWGN Channel 1

0.8

0.6

0.4

0.2

0

0.2

20 40 60 80 100 120 Correlation Lag

Figure 4.2: Performance of CC and GCC-PHAT in Presence of Gaussian Noise

contaminated gaussian noise. Figure 4.3 shows performance of 10% GCC-PHAT in presence of outlier data (An outlier is an observation that is distinctly diff tfrom bulk of the data). Clearly, several peaks are seen and we may conclude that the probability of incorrect estimation of time delay is very high.

Coming to another scenario where GCC fails to accurately estimate the time delay is in multipath channels. Shallow water channels being multipath dominated, signals travelling through acoustic channel suffer from multipath induced fading effects. Such multipath phenomenon leads to constructive as well as destructive interference and results in phase shift of the signal. The mathematical model of multipath scenario is commonly represented as:

x1(t) = s(t) h1,K (t) + n1(t)

x2(t) = s(t ) h2,K (t) + n2(t) (4.7)

here is the delay, K is the number of paths taken by source signal to reach the receiver. n1(t), n2(t) represent white gaussian noise and h1(t), h2(t), the impulse response of underwater channel as seen by the two receivers in Figure-4.4.

CC GCCPHAT

corr

elat

ion

peak

39

Comparison of CC and GCC in Presence of Outliers 1

0.5

0

0.5

1 20 40 60 80 100 120

Correlation Lag

0.05

0.04

0.03

0.02

0.01


Figure 4.3: Performance GCC-PHAT in Presence of Outlier Data

Figure 4.4: Multipath Scenario

In Figure-4.5 it may be seen that performance of Cross Correlation and GCC- PHAT are degraded in multipath environment [35]. Thus we conclude that there is a strong need to improve the performance of available TDE based localization in underwater scenario.

CC

GCCPHAT

corr

elat

ion

peak

co

rrel

atio

n pe

ak

40

4.1. Order Statistics Based Approach

i

i

Comparison of CC and GCC in Multipath Scenario 1

0.5

0


0.06 0.05 0.04 0.03 0.02 0.01


Figure 4.5: CC and GCC-PHAT Performance in Multipath Scenario

Methods to Improve Time Delay Estimation for Underwater Acous- tic Source Localization : It is evident that the performance of GCC-PHAT degrades in presence of multipath environment and impulsive noise. To improve estimation of time delay in such environments, we propose two approaches:

A) Order statistics based approach B) Signal detection based approach

Order Statistics Based Approach

Order statistics

Suppose that (X1,...,Xn) are n jointly distributed random variables. The corresponding orderstatistics are the XI s arranged in non decreasing order. The smallest of the XI s denoted by X1:n the second smallest is denoted by X2:n,..., and,

fi , the largest is denoted by Xn:n. Thus X1:n X2:n .... Xn:n.

CC

GCCPHAT

Cor

rela

tion

peak

C

orre

latio

n pe

ak

41

4.2. Signal Detection Based Approach

Applications of Order Statistics

(a) Robust Location Estimates Suppose that n independent measurements are available, and we wish to estimate their assumed common mean. It has long been recognized that the sample mean, though attractive from many viewpoints, suffers from an extreme sensitivity to outliers and model violations. Estimates based on the median or the average of central order statistics are less sensitive to model assumptions. A particularly well- known application of this observation is the accepted practice of using trimmed means (ignoring highest and lowest scores)in evaluating Olympic fi skating performances. (b) Detection of Outliers If one is confronted with a set of measurements and is concerned with determining whether some have been incorrectly made or reported, attention naturally focuses on certain order statistics of the sample. Usually the largest one or two and/or the smallest one or two are deemed most likely to be outliers. Typically we ask ques- tions like the following: If the observations really were iid, what is the probability that the latgest order statistic would be as large as the suspiciously large value we have observed?.

In this method we propose to improve the performance of GCC-PHAT in channels modeled with impulsive noise by exploiting the feature of order statistics of a sample. Going by the proposed method, We fi fi order statistics of the sample obtained at the two sensors, independently. Once order statistics are obtained, as a next step we replace the fi few and last few order statistics by the assumed (underlying) noise by choosing outliers from order statistics of the observed sample. Finally re-order the data as it was acquired. In other words we substitute the outlier data by assumed noise. The point we have here is that impulsive noise is responsible for multiple peaks when signal is such masked in noise. However it is observed that order statistics based approach can only subdue multiple peaks in impulsive noise, but not in multipath environment.

Signal Detection Based Approach

In previous subsection, we have suggested to use order statistics to improve the performance of GCC-PHAT in presence of impulsive noise. However, it was observed order statistics based method does not to improve the accuracy of time

42

4.2. Signal Detection Based Approach

delay estimation in case of a multipath dominated environment. In order to improve the accuracy of localization through TDE methods, it is felt that one may also view the time delay estimation problem to be a detection problem. The point we have here is that - in TDE based localization, one fi the time delay between receiving signals at diff t sensors. That is if one sensor, S1 has received the transmitted signal, then the other, S2 does not receive till some time, which depends on geometrical arrangement of sensors. It is for this reason under this proposition, we propose to view the TDE problem as a detection problem. Assum- ing the fi sensor to have received a signal, the second sensor receives only noise under H0 for a time duration D, and the signal after this period. With this hy- pothesis, we may devise a binary detection problem at the second sensor as follows:

Under H0: x(n) = w(n), for duration D

Under H1: x(n) = A + w(n), after duration D

where n : 0, 1, 2, 3, . . . . N-1. In the above devised detection problem, x(n) represents the received signal at sensor S2 (assuming S1 has received a signal D secs ago, A represents the presence of a signal and w(n) is Additive White Gaussian Noise (AWGN). According to [33], a Neyman Pearson likelihood ratio test (LRT) decides H1, if

p(X; H1) > (4.8) p(X; H0)

Since w(n) follows a gaussian distribution, the LRT detection statistic, T(X) boils down to,

N

T (X) = )

xi > (4.9) i=1

where = NA + 2 ln. For a given probability of false alarm (P ), threshold 2 A F A

is: = N Q1(PF A). However it needs to be mentioned here that the purpose

of detection in present scenario is to estimate the time delay estimate between two signals at the receivers. For this we defi a window length, by which we compute equation-4.9 on this window. In computing , we substitute N by WL, where WL is the window length to compute T(X). If T(x) does not cross the threshold , the window is moved by one sample and an initialized count is incremented by one (1). As the window is moved progressively, the count accumulates and when the signal is detected (for one of the shifts), the accumulated count amounts to estimated time delay.

43

4.3. Performance Improvement of GCC-PHAT in Presence of Impulsive Noise: Proposed Order Statistics Based Approach

We shall demonstrate that LRT based approach improves accuracy of estimated time delay as compared to GCC-PHAT in multipath environments as well as in presenc

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On Underwater Acoustic Communication & Source Localization