One of the currently exciting areas in SONAR
research is in the use of acoustic channel models to improve SONAR systems. In this paper, we use the echo pattern at a single phone to localise a sound source in both range and depth in the ocean. To make the localisation robust, the signal processing is
set-up with particular regard to which acoustic features are reliable and clear encoders of the source position. The resulting algorithm was tested using experimental data collected during INTIMATE’96- a shallow water experiment
conducted on the Portuguese continental shelf[1]. The results show successful tracking over a period of several days encompassing two fixed 25-hour stations and an 18-hour period of ship manoeuvres.This work was supported in part by ONR Grant
N00014-95-0558. One of us (M.B.P) gratefully
acknowledges support under the PRAXIS program
as a visiting Professor at the Universidade o
Algarve and by the New Jersey Institute of
Technology under the sabbatical program. The data was collected on a portable array lent to us by the SACLANT Undersea Research Centre

Coelho, E.

Démoulin, X.

Jesus, S. M.

Porter, M. B.

Stephan, Y.

Universidade do Algarve

Page 1/8
USING THE ECHO PATTERN TO RANGE A SOUND SOURCE
Michael B. Porter Sergio Jesus
Scripps Institution of Oceanography UCEH - University of Algarve, PT-8000,
La Jolla, CA 92152, U.S.A. Faro, Portugal
Yann Stéphan and Xavier Démoulin Emanuel Coeho
CMO - SHOM Instituto Hidrografico
Brest, France Lisboa, Portugal
Abstract
One of the currently exciting areas in SONAR
research is in the use of acoustic channel models to
improve SONAR systems. In this paper, we use the
echo pattern at a single phone to localise a sound
source in both range and depth in the ocean. To
make the localisation robust, the signal processing is
set-up with particular regard to which acoustic
features are reliable and clear encoders of the
source position. The resulting algorithm was tested
using experimental data collected during
INTIMATE’96- a shallow water experiment
conducted on the Portuguese continental shelf[1].
The results show successful tracking over a period
of several days encompassing two fixed 25-hour
tations and an 18-hour period of ship manoeuvres.
INTRODUCTION
Our interest in this paper is to develop a practical
method for rapid (immediate) source localisation in a
passive acoustic system that may operate in a highly
variable shallow-water environment. In brief, the
method consists of using an acoustic model[2] to
predict the echo pattern in the ocean that would
result if the source were anywhere on a grid of
hypothetical source locations. The source position is
then identified as the one whose echo pattern most
closely matches the measured one. This is
essentially the concept of matched-field
processing[3]. That processing is usually done with
tonals; however, there is also limited work with
broadband, time-domain signals [4][5][6][7].
The key challenge in incorporating acoustic models
is to do so in a robust manner. The acoustic models
are imperfect, primarily because of the lack of
precise environmental information. The signal
processing needs to incorporate the acoustic
information taking into account its quality. Our
approach is to correlate the logs of the envelopes of
the acoustic signals. This eliminates sensitivity to
the phase of surface and bottom reflections. It also
brings into balance the weaker late arrivals with the
stronger early arrivals so that the entire arrival
structure is used for source localisation.
EXPERIMENT OVERVIEW
To study this process, we conducted an experiment
called INTIMATE ‘96 for Internal Tide Monitoring by
means of Acoustic Tomography Experiment about
10 km west of Lisbon, Portugal. It involved a towed
source whose sound field was received by a fixed,
vertical line array with 4 hydrophones. The
configuration is illustrated in Figure 1. The source
transmitted linear frequency-modulated (LFM) chirps
sweeping from 300 Hz to 800 Hz over a 2-second
period. These were repeated every 8 seconds for the
duration of the experiment.
The sound source was towed at a depth of 90 m in a
sort of bow-tie pattern around the array as shown in
Figure 2. This pattern allowed us to study the
reliability of the tracking over ranges from 0 to 10
km. In addition, it allowed us to study the tracking on
several different radials. In particular, we included
an easy slice along the continental shelf and
therefore with a nearly constant bottom depth. At the
other extreme, we included a slice perpendicular to
the continental shelf, which has significant
topographic variation.
In addition, to the topographic variation there was
also (by design) great oceanographic variation due
to the internal tides. The crests of these tides are
aligned parallel to the shore so that slices along the
continental shelf are also range-independent in
terms of the oceanography.
Page 2/8
Figure 1: Experimental configuration.
Figure 2: Source track and array configuration relative to the bottom topography.
Page 3/8
Figure 3: Echo response over the 18 hours of ship manoeuvres.
Acoustics
The chirps were processed using traditional
methods to compress the waveforms into impulses.
In particular, the received time series were
correlated with the transmitted waveform to yield
the so-called correlogram. We next calculated the
envelope of the correlogram and converted the
result in dB. This process is repeated for every
transmitted chirp over the 18 hours of the
experiment yielding the plot shown in Fig. 3.
We see in this figure a pattern of echoes
corresponding to multiple surface and bottom
reflections, i.e. the multipath structure of the
channel response. We can understand this pattern
both qualitatively and quantitatively with the aid of a
ray/beam model. Figure 4 shows the rays
propagating from the lowest phone on the array at
115 m in the downslope direction. We see that
there are two classes of ray paths. The first group
of rays is refracted away from the warmer waters
near the ocean surface and thus never reflects from
the surface. The second group of rays propagates
steeply enough to penetrate the warm surface layer
and reflects off both the surface and bottom.
Perhaps the most obvious feature in the echo
response is the change in the overall duration of the
echo response. At longer ranges there are more
echoes and the overall channel response extends
to about 0.7 seconds. The duration tells us the
range of source while the precise pattern of echoes
tells us its depth.
Page 4/8
Figure 4: Ray trace and transmission-loss.
Tracking
As mentioned earlier, we track the source by
running an acoustic model for an ensemble of
candidate ranges and then we look for a match
between the modelled and measured acoustic
response. The acoustic model yields the ensemble
of predicted echo responses. For each transmitted
chirp we then compute the model/data correlation
for each candidate depth and range. Thus we
obtain correlation over a space-time volume.
Mathematically, we may write this as:
ttt dzrtgrzrtc leleò -= ),;()(),;( .
Here,
( )[ ] })(log20,0max{)( atrenvtrle -= ,
and r(t) is the waveform computed by correlating
the received waveform with a replica of the
transmitted waveform. The constant, a, is a clipping
threshold set at 30 dB. In addition,
( )[ ] }),;(log20,0max{),;( azrtgenvzrtgle -= ,
where g(t;r,z) is the computer generated model of
the correlogram. That modelled correlogram
depends on the hypothesised source range, r, and
depth, z, which allows us to localise the source. In
particular, searching for the maximum of the
correlation,
),;(max),( zrtczrP
t
= ,
identifies the best fit.
In Figure 5 below, we have plotted this correlation
after projecting by taking a maximum over depth.
There are 4 phones on the vertical array each
yielding its own ambiguity surface plotted in the
panels of Figure 5.
We observe that all 4 phones show a zigzagging
peak, which identifies the source range. The results
have been checked against GPS navigational data
and are correct to within 10%. We also note that
peak is a bit obscured during the time period
between 14:00 and 18:00. This corresponds to the
time when the source was located downslope. The
acoustic modelling here was done using a simple
flat-bottom model so this degradation was
expected.
Finally, we see that the upper right-hand panel
shows significantly poorer performance. This phone
actually failed during the experiment and was
flooded with salt water. Thus, these 4 panels show
the tracking being done with a simple range-
independent model in an environment that has
strong range-variation (due to both topography and
oceanography) and in a situation with very poor
Page 5/8
signal-to-noise ration. The robustness of the
processor is very gratifying.
Summary
Collectively these results suggest that acoustic
channel models offer great potential for developing
new systems. A critical issue is that of designing the
algorithms which exploit the reliable features of the
acoustic propagation. The INTIMATE ’96
experiment has provided a wealth of data that
illustrates the basic channel stability and provides
insight on how to best use the resulting information.
In fact many researchers believed that model-
based tracking would not be feasible in shallow-
water. The nature of shallow-water sound speed
profiles usually implies that all multipaths are
bottom interacting and the reflectivity of the ocean
bottom is usually not well known. Furthermore,
shallow waters typically show greater
oceanographic variability due to river efflux or other
coastal processes such as the tides (which play a
strong role in this experiment).
The simple algorithm described here takes many of
these physical features into account. The envelope
is in insensitive to the phase of the waveform.
Thus, the acoustic model can be inaccurate in its
prediction of the phase of the bottom reflection
coefficient.
The correlation of the logs is motivated by the fact
that most of the acoustic energy is carried in the
first few arrivals. These are not separated in time
and are very sensitive to oceanographic variation.
As such a simple correlation of time-series yields
an unreliable localizer. Correlation in the log
domain accentuates the role of the later arrivals,
which do provide reliable information for source
localisation.
To make this formal we may envision a statistical
ensemble of modelled echo responses derived from
another ensemble of possible environments. The
ensemble of possible environments would reflect
our prior estimates of oceanographic variability and
uncertainty in bottom properties. This naturally
leads to a Bayesian estimator, which however is
impractical to implement. The processor
implemented here may be viewed as a practical,
physics-based approximation of the optimal
processor.
Acknowledgments
This work was supported in part by ONR Grant
N00014-95-0558. One of us (M.B.P) gratefully
acknowledges support under the PRAXIS program
as a visiting Professor at the Universidade do
Algarve and by the New Jersey Institute of
Technology under the sabbatical program. The data
was collected on a portable array lent to us by the
SACLANT Undersea Research Centre.
References
[1] X. Démoulin, Y. Stéphan, S. Jesus, E. Coelho,
and M. Porter, ‘INTIMATE96: A shallow-water
tomography experiment devoted to the study of
internal tides', Proceedings of the Shallow
Water Acoustics Conference, Beijing, China
(1998).
[2] F. Jensen, W. Kuperman, M. Porter and H.
Schmidt, Computational Ocean Acoustics,
American Institute of Physics, (1994).
[3] Tolstoy, Matched-Field Processing for
Underwater Acoustics, World Scientific,
Singapore, (1993).
[4] Evan K. Westwood and David P. Knobles,
‘Source track localization via multipath
correlation matching’, J. Acoust. Soc. Amer.,
102(5), pp. 2645-54 (1997).
[5] L.N. Frazer and P.I. Pecholcs, ‘Single-
hydrophone localization’, J. Acoust. Soc.
Amer., 88, pp. 995-1002 (1990).
[6] R.K. Brienzo and W.S. Hodgkiss, ‘Broadband
matched-field processing’, J. Acoust. Soc.
Amer., 94(5), pp. 2821-2831 (1993).
[7] Z.H. Michalopoulou, M.B. Porter, and J.
Ianniello, ‘Broadband source localization in the
Gulf of Mexico', Journal of Computational
Acoustics, 2(3), pp. 361–370 (1996).
Page 6/8
 Figure 5: Ambiguity surfaces showing the source range over time as calculated from each of the 4
phones in the array.


Using the echo pattern to range a sound source

Sapientia (Univ. do Algarve)

Page 1/8USING THE ECHO PATTERN TO RANGE A SOUND SOURCEMichael B. Porter Sergio JesusScripps Institution of Oceanography UCEH - University of Algarve, PT-8000,La Jolla, CA 92152, U.S.A. Faro, PortugalYann Stéphan and Xavier Démoulin Emanuel CoehoCMO - SHOM Instituto HidrograficoBrest, France Lisboa, PortugalAbstractOne of the currently exciting areas in SONARresearch is in the use of acoustic channel models toimprove SONAR systems. In this paper, we use theecho pattern at a single phone to localise a soundsource in both range and depth in the ocean. Tomake the localisation robust, the signal processing isset-up with particular regard to which acousticfeatures are reliable and clear encoders of thesource position. The resulting algorithm was testedusing experimental data collected duringINTIMATE’96- a shallow water experimentconducted on the Portuguese continental shelf[1].The results show successful tracking over a periodof several days encompassing two fixed 25-hourtations and an 18-hour period of ship manoeuvres.INTRODUCTIONOur interest in this paper is to develop a practicalmethod for rapid (immediate) source localisation in apassive acoustic system that may operate in a highlyvariable shallow-water environment. In brief, themethod consists of using an acoustic model[2] topredict the echo pattern in the ocean that wouldresult if the source were anywhere on a grid ofhypothetical source locations. The source position isthen identified as the one whose echo pattern mostclosely matches the measured one. This isessentially the concept of matched-fieldprocessing[3]. That processing is usually done withtonals; however, there is also limited work withbroadband, time-domain signals [4][5][6][7].The key challenge in incorporating acoustic modelsis to do so in a robust manner. The acoustic modelsare imperfect, primarily because of the lack ofprecise environmental information. The signalprocessing needs to incorporate the acousticinformation taking into account its quality. Ourapproach is to correlate the logs of the envelopes ofthe acoustic signals. This eliminates sensitivity tothe phase of surface and bottom reflections. It alsobrings into balance the weaker late arrivals with thestronger early arrivals so that the entire arrivalstructure is used for source localisation.EXPERIMENT OVERVIEWTo study this process, we conducted an experimentcalled INTIMATE ‘96 for Internal Tide Monitoring bymeans of Acoustic Tomography Experiment about10 km west of Lisbon, Portugal. It involved a towedsource whose sound field was received by a fixed,vertical line array with 4 hydrophones. Theconfiguration is illustrated in Figure 1. The sourcetransmitted linear frequency-modulated (LFM) chirpssweeping from 300 Hz to 800 Hz over a 2-secondperiod. These were repeated every 8 seconds for theduration of the experiment.The sound source was towed at a depth of 90 m in asort of bow-tie pattern around the array as shown inFigure 2. This pattern allowed us to study thereliability of the tracking over ranges from 0 to 10km. In addition, it allowed us to study the tracking onseveral different radials. In particular, we includedan easy slice along the continental shelf andtherefore with a nearly constant bottom depth. At theother extreme, we included a slice perpendicular tothe continental shelf, which has significanttopographic variation.In addition, to the topographic variation there wasalso (by design) great oceanographic variation dueto the internal tides. The crests of these tides arealigned parallel to the shore so that slices along thecontinental shelf are also range-independent interms of the oceanography.Page 2/8Figure 1: Experimental configuration.Figure 2: Source track and array configuration relative to the bottom topography.Page 3/8Figure 3: Echo response over the 18 hours of ship manoeuvres.AcousticsThe chirps were processed using traditionalmethods to compress the waveforms into impulses.In particular, the received time series werecorrelated with the transmitted waveform to yieldthe so-called correlogram. We next calculated theenvelope of the correlogram and converted theresult in dB. This process is repeated for everytransmitted chirp over the 18 hours of theexperiment yielding the plot shown in Fig. 3.We see in this figure a pattern of echoescorresponding to multiple surface and bottomreflections, i.e. the multipath structure of thechannel response. We can understand this patternboth qualitatively and quantitatively with the aid of aray/beam model. Figure 4 shows the rayspropagating from the lowest phone on the array at115 m in the downslope direction. We see thatthere are two classes of ray paths. The first groupof rays is refracted away from the warmer watersnear the ocean surface and thus never reflects fromthe surface. The second group of rays propagatessteeply enough to penetrate the warm surface layerand reflects off both the surface and bottom.Perhaps the most obvious feature in the echoresponse is the change in the overall duration of theecho response. At longer ranges there are moreechoes and the overall channel response extendsto about 0.7 seconds. The duration tells us therange of source while the precise pattern of echoestells us its depth.Page 4/8Figure 4: Ray trace and transmission-loss.TrackingAs mentioned earlier, we track the source byrunning an acoustic model for an ensemble ofcandidate ranges and then we look for a matchbetween the modelled and measured acousticresponse. The acoustic model yields the ensembleof predicted echo responses. For each transmittedchirp we then compute the model/data correlationfor each candidate depth and range. Thus weobtain correlation over a space-time volume.Mathematically, we may write this as:ttt dzrtgrzrtc leleò -= ),;()(),;( .Here,( )[ ] })(log20,0max{)( atrenvtrle -= ,and r(t) is the waveform computed by correlatingthe received waveform with a replica of thetransmitted waveform. The constant, a, is a clippingthreshold set at 30 dB. In addition,( )[ ] }),;(log20,0max{),;( azrtgenvzrtgle -= ,where g(t;r,z) is the computer generated model ofthe correlogram. That modelled correlogramdepends on the hypothesised source range, r, anddepth, z, which allows us to localise the source. Inparticular, searching for the maximum of thecorrelation,),;(max),( zrtczrPt= ,identifies the best fit.In Figure 5 below, we have plotted this correlationafter projecting by taking a maximum over depth.There are 4 phones on the vertical array eachyielding its own ambiguity surface plotted in thepanels of Figure 5.We observe that all 4 phones show a zigzaggingpeak, which identifies the source range. The resultshave been checked against GPS navigational dataand are correct to within 10%. We also note thatpeak is a bit obscured during the time periodbetween 14:00 and 18:00. This corresponds to thetime when the source was located downslope. Theacoustic modelling here was done using a simpleflat-bottom model so this degradation wasexpected.Finally, we see that the upper right-hand panelshows significantly poorer performance. This phoneactually failed during the experiment and wasflooded with salt water. Thus, these 4 panels showthe tracking being done with a simple range-independent model in an environment that hasstrong range-variation (due to both topography andoceanography) and in a situation with very poorPage 5/8signal-to-noise ration. The robustness of theprocessor is very gratifying.SummaryCollectively these results suggest that acousticchannel models offer great potential for developingnew systems. A critical issue is that of designing thealgorithms which exploit the reliable features of theacoustic propagation. The INTIMATE ’96experiment has provided a wealth of data thatillustrates the basic channel stability and providesinsight on how to best use the resulting information.In fact many researchers believed that model-based tracking would not be feasible in shallow-water. The nature of shallow-water sound speedprofiles usually implies that all multipaths arebottom interacting and the reflectivity of the oceanbottom is usually not well known. Furthermore,shallow waters typically show greateroceanographic variability due to river efflux or othercoastal processes such as the tides (which play astrong role in this experiment).The simple algorithm described here takes many ofthese physical features into account. The envelopeis in insensitive to the phase of the waveform.Thus, the acoustic model can be inaccurate in itsprediction of the phase of the bottom reflectioncoefficient.The correlation of the logs is motivated by the factthat most of the acoustic energy is carried in thefirst few arrivals. These are not separated in timeand are very sensitive to oceanographic variation.As such a simple correlation of time-series yieldsan unreliable localizer. Correlation in the logdomain accentuates the role of the later arrivals,which do provide reliable information for sourcelocalisation.To make this formal we may envision a statisticalensemble of modelled echo responses derived fromanother ensemble of possible environments. Theensemble of possible environments would reflectour prior estimates of oceanographic variability anduncertainty in bottom properties. This naturallyleads to a Bayesian estimator, which however isimpractical to implement. The processorimplemented here may be viewed as a practical,physics-based approximation of the optimalprocessor.AcknowledgmentsThis work was supported in part by ONR GrantN00014-95-0558. One of us (M.B.P) gratefullyacknowledges support under the PRAXIS programas a visiting Professor at the Universidade doAlgarve and by the New Jersey Institute ofTechnology under the sabbatical program. The datawas collected on a portable array lent to us by theSACLANT Undersea Research Centre.References[1] X. Démoulin, Y. Stéphan, S. Jesus, E. Coelho,and M. Porter, ‘INTIMATE96: A shallow-watertomography experiment devoted to the study ofinternal tides', Proceedings of the ShallowWater Acoustics Conference, Beijing, China(1998).[2] F. Jensen, W. Kuperman, M. Porter and H.Schmidt, Computational Ocean Acoustics,American Institute of Physics, (1994).[3] Tolstoy, Matched-Field Processing forUnderwater Acoustics, World Scientific,Singapore, (1993).[4] Evan K. Westwood and David P. Knobles,‘Source track localization via multipathcorrelation matching’, J. Acoust. Soc. Amer.,102(5), pp. 2645-54 (1997).[5] L.N. Frazer and P.I. Pecholcs, ‘Single-hydrophone localization’, J. Acoust. Soc.Amer., 88, pp. 995-1002 (1990).[6] R.K. Brienzo and W.S. Hodgkiss, ‘Broadbandmatched-field processing’, J. Acoust. Soc.Amer., 94(5), pp. 2821-2831 (1993).[7] Z.H. Michalopoulou, M.B. Porter, and J.Ianniello, ‘Broadband source localization in theGulf of Mexico', Journal of ComputationalAcoustics, 2(3), pp. 361–370 (1996).Page 6/8 Figure 5: Ambiguity surfaces showing the source range over time as calculated from each of the 4phones in the array.

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Using the echo pattern to range a sound source

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