Underwater Acoustics

142 Pages·2012·2.72 MB·English

by  S. Bourennane

UNDERWATER
ACOUSTICS

Edited by Salah Bourennane
Underwater Acoustics
Edited by Salah Bourennane

Published by InTech
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Copyright © 2012 InTech
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First published March, 2012
Printed in Croatia

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Additional hard copies can be obtained from [email protected]

Underwater Acoustics, Edited by Salah Bourennane
p. cm.
ISBN 978-953-51-0441-4

Contents

Chapter 1 A Novel Bio-Inspired Acoustic
Ranging Approach for a Better
Resolution Achievement 1
Said Assous, Mike Lovell, Laurie Linnett,
David Gunn, Peter Jackson and John Rees
Chapter 2 Array Processing: Underwater
Acoustic Source Localization 13
Salah Bourennane, Caroline Fossati and Julien Marot
Chapter 3 Localization of Buried Objects in
Sediment Using High Resolution
Array Processing Methods 41
Caroline Fossati, Salah Bourennane and Julien Marot
Chapter 4 Adaptive Technique for Underwater
Acoustic Communication 59
Shen Xiaohong, Wang Haiyan,
Zhang Yuzhi and Zhao Ruiqin
Chapter 5 Narrowband Interference Suppression
in Underwater Acoustic OFDM System 75
Weijie Shen, Haixin Sun, En Cheng,
Wei Su and Yonghuai Zhang
Chapter 6 CI/OFDM Underwater Acoustic
Communication System 95
Fang Xu and Ru Xu
Chapter 7 Iterative Equalization and Decoding
Scheme for Underwater Acoustic
Coherent Communications 121
Liang Zhao and Jianhua Ge

0
1
A Novel Bio-Inspired Acoustic Ranging Approach
for a Better Resolution Achievement
SaidAssous1,MikeLovell1,LaurieLinnett2,DavidGunn3,
PeterJackson3 andJohnRees3
1UltrasoundResearchLaboratory,UniversityofLeicester
2FortkeyLtd
3UltrasoundResearchLaboratory,BritishGeologicalSurvey
UnitedKingdom
1.Introduction
Bat and dolphin use sound to survive and have greatly superior capabilities to current
technology with regard to resolution, object identification and material characterisation.
Some bats can resolve some acoustic pulses thousands of times more efficiently than
current technology (Thomas & Moss, 2004 ). Dolphins are capable of discriminating
different materials based on acoustic energy, again significantly out-performing current
detectionsystems. Notonlyaretheseanimalssupremeintheirdetectionanddiscrimination
capabilities, they also demonstrate excellent acoustic focusing characteristics - both in
transmissionandreception. Ifitcouldapproachtheefficienciesofbatandcetaceansystems,
the enormous potential foracoustic engineering, has been widelyrecognised. Whilstsome
elements of animal systems have been applied successfully in engineered systems, the
latter have come nowhere near the capabilities of the natural world. Recognizing that
engineeredacousticsystemsthatemulatebatandcetaceansystemshaveenormouspotential,
we presentinthischapter abreakthroughinhigh-resolutionacousticimagingand physical
characterization basedonbio-inspiredtimedelayestimationapproach. Acriticallimitation
that is inherent to all current acoustic technologies, namely that detail, or resolution, is
compromised by the total energy of the system. Instead of using higher energy signals,
resultinginpoorersoundquality,randomnoiseanddistortion,theyintendtousespecifically
designedadaptablelowerenergy‘intelligent‘signals. Therearearound1000speciesofbats
aliveintheworldtoday. Thesearebrokendownintothemegabats,whichincludethelarge
fruitbats,andthemicrobats,whichcoverarangeofspecies,bothsmallandlarge,whicheat
insects,fruit,nectar,fish,andoccasionallyotherbats. Withtheexceptionofonegenus,none
ofthemegabatsuseecholocation, whileallofthe microbatsdo. Echolocationistheprocess
bywhichthebatsendsoutabriefultrasonicsoundpulseandthenwaitstohearifthereisan
echo. Byknowingthetimeofflightofthesoundpulse,thebatcanworkoutthedistanceto
thetarget;eitherpreyoranobstacle. Thatmuchiseasy,andthistypeoftechnologyhaslong
beenadoptedbyengineerstosenseobjectsatadistanceusingsound,andtoworkouthowfar
awaytheyare. However,batscandomuchmorethanthis,buttheextentoftheirabilitiesto
sensetheworldaroundthemislargelyunknown,andtheresearchoftencontradictory.Some
experimentshaveshownthatbatscantimepulses,andhenceworkoutthedistancetoobjects
22 UnderwWaillt-beer-s eAt-cbyo-INu-TsEtCicHs
with fargreateraccuracy than iscurrentlypossible,eventoengineers. Sonar isarelatively
recent invention by man for locating objects under water using sound waves. However,
locating objectsinwaterand airhasevolvedinthebiologicalworldtoamuchhigherlevel
ofsophistication. Echolocation,oftencalledbiosonar,isusedbybatsandcetaceans(whales,
manatees,dolphinsetc.) usingsoundwavesatultrasonicfrequencies(above20kHz). Based
on the frequenciesin the emitted pulses, some bats can resolvetargetsmany times smaller
than should be possible. They areclearlyprocessingthe sound differentlyto currentsonar
technology. Dolphins are capable of discriminating different materials based on acoustic
energy,againsignificantlyout-performingcurrentdetectionsystems. Acompletereviewof
thiscapabilitiescanbefoundin(Whitlow,1993).Notonlyaretheseanimalssupremeintheir
detection and discriminationcapabilities, they also demonstrate excellentacoustic focusing
characteristics-bothintransmissionandreception. Whatwecangainfromtheseanimalsis
howtolearntoseeusingsound.Thisapproachmaynotleadusdownthetraditionalrouteof
signalprocessinginacoustic,butitmayletusexploredifferentwaysofanalyzinginformation,
inasense,toasktherightquestionratherthanlookfortherightanswer.
This chapter presents a bio-inspired approach for ranging based on the use of phase
measurement to estimate distance (or time delay). We will introduce the technique with
examples done for sound in air than some experiments for validation are done in tank
water. Themotivationforthiscomesfromthefactthat batshave beenshowntohave very
goodresolutionwithregardtotargetdetectionwhensearchingduringflight. JimSimmons
(Whitlow&Simmons,2007)hasestimatedforbatsusingapulsesignalwithacentrefrequency
of about 80 kHz (bandwidth 40 kHz) can have a pulse/echo resolution of distance in air
approaching afewmicrons. Forthisfrequency,the wavelength(λ)ofsoundinairisabout
4mm,andsousingthehalfwavelength(λ/2)astheguideforresolutionweseethatthisis
about200timeslessthanthatachievedbythebat. Wedemonstrateinthischapterhowwe
havebeeninspiredfrombatanditsusedsignal(chirp)toinferabetterresolutionfordistance
measurementbylookingtothephasedifferenceoftwofrequencycomponents.
2.Timedelayanddistancemeasurementusingconventionalapproaches
Considering a constant speed of sound in a medium, any improvement in distance
measurementbasedonacoustictechniqueswillrelyontheaccuracyofthetimedelayorthe
time-of-flightmeasurement. Thetimedelayestimationisalsoafundamental stepinsource
localization or beamforming applications. It has attracted considerable research attention
over the past few decades in different technologies including radar, sonar, seismology,
geophysics,ultrasonics,communicationandmedicalultrasoundimaging.Varioustechniques
are reportedinthe literature(Knapp &Carter,1976; Carter,1979; 1987; Boucher &Hassab,
1981; Chen et al., 2004) and a complete review can be found in (Chen et al., 2006). Chen
et.al in their review consider critical techniques, limitations and recent advances that have
significantly improved the performance of time-delay estimation in adverse environments.
They classify these techniques into two broad categories: correlator-based approaches and
system-identification-based techniques. Both categories can be implemented using two or
moresensors;ingeneral,moresensorsleadtoincreaserobustnessduetogreaterredundancy.
Whenthetimedelayisnotanintegralmultipleofthesamplingrate,however,itisnecessary
to either increasethe samplingrate oruseinterpolationboth having significant limitations.
Interpolating by using a parabolic fit to the peak usually yields to a biased estimate of the
time delay, with both the bias and variance of the estimate dependent on the location of
AAN NoveolvBieo-lIn sBpiiroed-AIncosupstiicrReadng iAngcAoppurosatcihcfo RraaBenttgerinRegso AlutiponpArcohiaevcemhe nftor a Better Resolution Achievement 33
thedelaybetweensamples,SNR,signalandnoisebandwidths,andtheprefilterorwindow
usedinthegeneralizedcorrelator. Increasingthesamplingrateisnotdesirableforpractical
implementation, since sampling at lower rates is suitable for analog-to-digital converters
(ADCs) that are more precise and have a lower power consumption. In addition, keeping
the samplingrate lowcanreducethe loadonbothhardwareand furtherdigitalprocessing
units.
Inthischapter,wepresentanewphase-basedapproachtoestimatethetime-of-flight,using
onlythereceivedsignalphaseinformationwithoutneedtoareferencesignalasitisthecase
for other alternative phase-based approaches often relying on a reference signal provided
by a coherent local oscillator (Belostotski et al., 2001) to count for the number of cycles
taking the signal to travel a distance. Ambiguities in such phase measurement due to the
inability to count integer number of cycles (wavelengths) are resolved using the Chinese
RemainderTheorem(CRT)takenfromnumbertheory,wherewavelengthselectionisbased
on pair-wise relatively prime wavelengths (Belostotski et al., 2001; Towers et al., 2004).
However, the CRT is not robust enough in the sense that a small errors in its remainders
may inducealargeerrorinthe determinedintegerbythe CRT. CRTwith remaindererrors
has been investigated in the literature (Xiang et al., 2005; Goldreich et al., 2000). Another
phase-based measurement approach adopted to ensure accurate positioning of commercial
robots, uses two or more frequencies in a decade scale in the transmitted signal. In this,
the phase shift of the receivedsignal with respectto the transmitted signal is exploitedfor
ranging (Lee et al., 1989; Yang et al., 1994). However, this approach is valid only when
the maximum path-length/displacement is less than one wavelength, otherwise a phase
ambiguity willappear. The time delayestimationapproachproposedhere, isbased onthe
useoflocalphasedifferencesbetweenspecificfrequencycomponentsofthereceivedsignal.
Using this approach overcomes the need to cross-correlate the received signal with either
a reference signal or the transmitted signal.The developed novel approach for time delay
estimation,hencefordistanceandspeedofsoundmeasurementoutperformtheconventional
correlation-based techniques and overcomes the 2π-phase ambiguity in the phase-based
approachesandmostpracticalsituationscanbeaccommodated(Assousetal.,2008;2010).
3.Distancemeasurementusingthereceivedsignalphasedifferencesbetween
components: newconcept
0.2
0.15
0.1
0.05
Amplitude−0.050
−0.1
−0.15
−0.2
−0.250 50 100 150 200 250 300 350 400
Sample (N)
(a) Batpulse. (b) Time-Frequency plot of the bat
pulse.
Fig.1
44 UnderwWaillt-beer-s eAt-cbyo-INu-TsEtCicHs
Consider the time-frequency plot of a single bat pulse shown in Fig.1, we note that at any
particulartimewithinthepulsethereareessentiallytwofrequenciespresent.Thepulselength
isabout2msandthefrequencybandwidthisabout40kHz.Letdescribeinthefollowinghow
usingtwoormorefrequencieswemayinferadistance.
Toexplaintheconcept,considerascenariowhereanacousticpulsecontainsasinglefrequency
component f with an initial zero phase offset. This pulse isemitted throughthe medium,
1
impinges on a target, is reflected and returns back. The signal is captured and its phase
measured relative to the transmitted pulse. Given this situation, we cannot estimate the
distancetoandfromanobjectgreaterthanonewavelengthaway(hence,usually,wewould
estimatethetimeofarrivalofthepulseandassumeavalueforthevelocityofsoundinthe
mediumtoestimatethedistancetothetarget).
Forsimplicity,assumethepulsecontainsasinglecycleoffrequency f ofwavelengthλ .
1 1
ThedistanceDtothetargetcanbeexpressedas
D=n λ +r (1)
1 1 1
whereλ = v/f ,n isaninteger,r isafractionofthewavelengthλ andvisthespeedof
1 1 1 1 1
soundinthemedium.
r canbeexpressedasfollows
1
φ
r =λ × 1 (2)
1 1
360
where φ is the residual phase angle in degrees. Combining equations (1) and (2) and
1
rearranging
φ
D=n λ +λ 1
1 1 1360
v φ v
=n + 1
1f 360 f
1 1
v φ
D= (n + 1 ) (3)
f 1 360
1
Ifwetransmitasecondfrequencycomponent f withinthesamepulse,thenitwillalsohave
2
associatedwithitawavelengthλ andaresidualphaseφ ,similarly:
2 2
v φ
D= (n + 2 ) (4)
f 2 360
2
Equations(1)and(2)canbesolvedbyfinding(2)−(1)×(λλ2)andrearrangedtogive
1
λ λ (φ −φ )
D=( 1 2 )((n −n )+ 2 1 )
λ −λ 2 1 360
1 2
λ λ Δφ
D=( 1 2 )(Δn+ ) (5)
λ −λ 360
1 2
Usingv= f ×λweobtain
v (φ −φ ) v Δφ
D= ((n −n )+ 2 1 )= (Δn+ ) (6)
f − f 2 1 360 Δf 360
2 1