SOURCE LOCALIZATION IN A HIGHLY VARIABLE SHALLOW
WATER ENVIRONMENT: RESULTS FROM ASCOT-01
MARTIN SIDERIUS
SAIC, 1299 Prospect St., La Jolla, CA 92037
E-mail: [email protected]
PETER NIELSEN
SACLANT Undersea Research Centre, Viale S. Bartolomeo 400, 19138 La Spezia, Italy
E-mail: [email protected]
JÜRGEN SELLSCHOPP
Forschungsanstalt der Bundeswehr für Wasserschall und Geophysik,
Klausdorfer Weg 2-24, 24148 Kiel, Germany
E-mail: [email protected]
Variability in the ocean environment can have a big impact on acoustic propagation.
Acoustic receptions often contain multipath contributions with fluctuations that vary
significantly from the direct path to the higher order multipath. Matched-field methods
take advantage of the multipath to extract information about the source location and
seabed properties. Matched-field processing is generally successful in environments that
are not highly range dependent and do not vary significantly in time. However, in some
cases the environmental conditions are too extreme for good propagation predictions and
matched field results suffer. The ASCOT-01 acoustic experiments were conducted in
June 2001 specifically to explore the limits of matched field methods in highly variable
environments. Measurements were made over several days between a sound source
and a moored vertical line array of receivers. Results characterizing the difficulties of
matched field processing at this site will be presented. Source localization results using
standard estimators will be compared with those using new alternatives intended to be
robust against harsh environmental conditions.
1 Introduction
Matched-field processing (MFP) is a beamforming method that can take advantage of multipath propagation environments to extract information about the location of the sound
source. It is usually applied in the ocean where multipath can degrade planewave beamforming yet enhance MFP results. The MFP technique uses a numerical propagation
model to produce the beamforming replica fields for all possible source locations (instead
of using planewave replicas) >d, 1H. With a vertical line array (VLA), MFP beamforming
can estimate both source range and depth which also gives it an advantage over planewave
beamforming. However, MFP often fails due to a lack of detailed environmental data
needed for the propagation models. For instance, if the assumed seabed sound speed
425
N.G. Pace and F.B. Jensen (eds.), Impact of Littoral Environmental Variability on Acoustic Predictions and
Sonar Performance, 425-432.
© 2002 Kluwer Academic Publishers. Printed in the Netherlands.
426
M. SIDERIUS ET AL.
1
0
70
74
Depth (m)
20
0.5
76
30
Depth (m)
72
10
78
40
0
50
−200
0
∆ Range (m)
80
200
60
70
80
1
1.5
2
Range (km)
2.5
3
Figure 1. Source localization ambiguity surface for the Advent’99 site. Source position corresponds
to the red spot at approximately 2 km range and 74 m depth. Panel on the right shows the position
of the highest peak in the ambiguity surface for all data that were processed over the 5 hours of
data collected. Because the search contained discrete locations, many source position estimates fall
on top of each other. Note: the colors in the left panel are scaled to the maximum value.
is incorrect, the replica fields will contain errors that degrade the beamforming results.
This can partially be mitigated by considering a larger set of replicas that includes all
possible environments for all possible source positions. This would produce an enormous
set of replica fields but, in practice, this can be done with a relatively small number using an optimization, or “focalization” procedure [3]. The focalization procedure searches
for environments with replicas yielding a higher beamformer output. The added benefit
of focalization is to simultaneously produce an estimate of the unknown environmental
properties such as seabed sound speed. When the source location is known, the search
is only for environmental properties and this is the basis of MFP geoacoustic inversion.
Matched-field source localization and geoacoustic inversion were tested using data
collected during the Advent’99 experiments [4, 5]. These experiments were conducted on
the Adventure Bank in the Strait of Sicily in May 1999. Acoustic data were collected
on a VLA from sources transmitting in the 200–1600 Hz frequency band. The source
was localized using data from transmissions taken over 5 hours containing 6 tones (200–
700 Hz in 100 Hz increments) and linear frequency modulated (LFM) sweeps (200–800
Hz). An example of source localization for the Advent’99 site is shown in Fig. 1. The
localization results shown used the multi-tone transmissions but the results were nearly
identical for the LFM data. The figure indicates excellent localization of the source using
data collected over 5 hours.
In addition to localization, the Advent’99, 2-km data was processed for geoacoustic
properties using matched-field inversion. As with the localization results, the estimated
geoacoustic properties were extremely stable for all the data inverted over the 5 hours.
However, this was not true for the data taken at 10 km. Variability (both temporal and
spatial) in the ocean sound speed could not be included in the range-independent replica
modeling and this caused errors that were sufficient to prevent the search algorithm from
finding the correct seabed properties. The 10-km geoacoustic inversion results differed
from those at 2 km and erroneously changed with time as a result of changes in the ocean
sound speed profile. It was concluded that unless the impact of variability could be
compensated for in the modeling, MFP geoacoustic inversion was better suited for short
SOURCE LOCALIZATION IN A VARIABLE ENVIRONMENT
427
range measurements [5]. Even with the instability in the 10-km geo-acoustic inversion
results the source was correctly localized over most of the 18 hours of data. To summarize
the Advent’99 results, the localization was extremely stable for data below 700 Hz and
at all ranges (with a few outliers at 10 km range). Recent work with the Advent’99 data
has shown that all data could be successfully localized if the ocean sound speed profile
is included in the focalization procedure [7]. That is, the ocean variability could be
compensated for by optimizing a parameterized ocean sound speed profile. These recent
results show good localization results at 10 km even for frequencies as high as 1500 Hz.
For this reason the Advent’99 site is considered a relatively forgiving environment for
MFP which is not the case for the ASCOT-01 site described in the next section.
2
The ASCOT-01 acoustic experiments
The ASCOT-01 acoustic experiments took place June 12–18, 2001 off the coast of New
England. The experiment design was similar to that of Advent’99 and ideal for testing
MFP localization and geoacoustic inversion. A 64-element VLA was moored at ranges of
approximately 1, 2, 5 and 10 km from the sound source. The water depth averaged 102 m
and the VLA spanned depths of 28–94 m. The sound source was about 4 m from the
bottom as it was mounted in a steel frame tower that was sitting on the seabed. The NATO
Research Vessel ALLIANCE was used to both power the sound source and receive the VLA
data by radio telemetry. To capture the oceanographic conditions, extensive environmental
measurements were made: three moored thermistor strings measured the temperature
profile and an Acoustic Doppler Current Profiler (ADCP) was used to estimate ocean
currents. In addition, a vertical chain containing conductivity, temperature and pressure
(CTD) sensors was towed along the acoustic tracks and later processed for sound speed.
The CTD chain covered the water column down to about 70 m. Below that depth there
was very little change in the sound speed. The ASCOT-01 site had a sound speed profile
that changed over depth by about 40 m/s compared to the Advent’99 site that changed by
about 5 m/s. A more detailed comparison of the relationship between the acoustic data
and environmental condition for these two shallow water sites can be found in Ref. [6].
3
Matched field processing of ASCOT-01 data
Central to MFP is the correlation function that quantifies the agreement between the
measured field and the computed replicas. A common correlation function (often called
the Bartlett Processor) is given below:
"NH
NF
| i=1
pi (ωj )qi (ωj )∗ |2
1 !
B=
,
(1)
"NH
" H
2
NF j=1 i=1 |pi (ωj )|2 N
i=1 |qi (ωj )|
where NF is the number of frequency components, NH is the number of hydrophones
and the measured and modeled complex pressure vectors (at frequency ωj ) are pi and
qi (∗ denotes the complex conjugate operation). This correlator has a value of 1 for a
perfect match between measured data and replica and 0 when uncorrelated. This is the
processor used to produce the ambiguity surface in Fig. 1.
A focalization procedure similar to that used to produce Fig. 1 for the Advent’99, 2-km
data was applied to the ASCOT-01 data at the same range. That is, replicas were generated
428
M. SIDERIUS ET AL.
Figure 2. Left panel shows the measured, band limited impulse response data on the VLA as a
function of time. Acoustic paths that travel at steeper angles have longer paths and arrive later in
time. Middle panel shows the result of simulating the received time series using the environment
found in the focalization MFP. Right panel is also a simulation of the received time series except with
the seabed fixed to be 1750 m/s. Both simulation use ray-trace propagation model BELLHOP [10].
(Relative amplitudes are shown on a log scale with colors spanning 30 dB).
for all possible source positions as well as for many environmental conditions using the
normal mode propagation model SNAP [8]. A genetic algorithm was used to direct the
focalization search [9]. As was done for the Advent’99 analysis, the ocean channel was
assumed range-independent and focalization included the seabed sound speed, density
and attenuation as well as the water depth. Data from the LFM transmissions were used
at frequencies of 225–725 Hz in 50 Hz increments (similar results were found using
multi-tone data in the same frequency band).
In contrast to the Advent’99 results, the source was not correctly localized. The most
likely source depth was found to be 27 m instead of 98 m and there were no secondary
peaks anywhere near the true source position. Further, the correlation (Eq. (1)) produced a
value of only 0.3 compared to 0.8–0.9 for the Advent’99 data. The focalization results for
the seabed properties indicated a slow sound speed seabed which was different from that
expected (sediment maps of the area showed a highly reflective material typical of a fast
sound speed). The seabed sound speed determined from the focalization was 1540 m/s
which was near the lower limit of the search interval. A plot of the measured, band
limited (200–800 Hz), impulse response on the VLA together with the modeled time
series using the focalized environment is shown in the left two panels of Fig. 2. Clearly,
the modeled time series does not re-create the arrival pattern seen in the data. The late
arrivals correspond to steep angles of propagation. The presence of these implies a highly
reflective seabed. Shown in the right panel of Fig. 2 is an improved model for the time
series found after just a few attempts at adjusting the sound speed in the seabed (results
shown are for 1750 m/s).
Although the data from Advent’99 produced completely stable results for geoacoustic
properties and localization, the results for the ASCOT-01 site are very different. It is
clear from Fig. 2 that the focalization process did not work and the seabed properties are
better modeled with a fast (1750 m/s) sound speed. Would using this ad hoc value for the
seabed improve MFP localization? The answer to that question is given by the ambiguity
surface in right panel in Fig. 3 where a fixed environment was used with seabed sound
speed of 1750 m/s. As before, there is no evidence of the source that should appear at a
range of about 1.85 km and depth of 98 m. The data collected at 1.85 km was intended
429
SOURCE LOCALIZATION IN A VARIABLE ENVIRONMENT
Depth (m)
1
1
20
0.8
20
0.8
40
0.6
40
0.6
60
0.4
60
0.4
80
0.2
80
0.2
100
0.5
0
1
Range (km)
1.5
100
1.5
0
2
Range (km)
2.5
Figure 3. MFP source localization ambiguity surface (using SNAP) for data collected during
ASCOT-01. Left panel is the results for the source at about 0.78 km and the right panel for
source at about 1.85 km (depth about 98 m). It appears possible to localize the source for the data
at 0.78 km but not for 1.85 km.
to provide a sanity check for the MFP localization at close range. However, localization
difficulties at that range were already becoming obvious from analysis that took place
during the experiments. Therefore, a new sanity check range was chosen at 1 km (which
after deployments turned out to be 0.78 km). Localization results for that range provided
the sanity check and are shown in the left panel of Fig. 3.
4
An alternative matched field processor
As shown, information can be inferred about the environment by simply making observations about the arrival structure (Fig. 2). To match the extent of multipath arrivals seen
with the ASCOT-01 data the seabed must support steep angle propagation and therefore is highly reflective. The measured arrival structure along the array was reasonably
well matched using a manual process of changing seabed properties and observing the
ray-trace, time series. In this sense the manual process out-performed MFP geoacoustic
inversion. The question is: why are the MFP results not producing the correct source
location or the correct environment? And, can the information in plots like Fig. 2 be
used to better localize and determine geoacoustic properties? The answer to the first
question is addressed in Sect. 5 and in Ref. [11]. The second question requires defining a
new correlation function that compares quantities similar to those shown in Fig. 2. This
requires a cross-correlation of the envelope of the pressure time-series which can be done
at each hydrophone depth. To make use of the full array these correlation values can be
added together. Or,
Corr =
NH
1 !
R(|E(pi (tj ))|; |E(qi (tj ))|)2 ,
NH i=1
(2)
where NH is the number of hydrophones, p(tj ) is the discrete-time, measured pressure,
q(tj ) is the modeled time series, R represents taking the cross-correlation maximum value
and E represents taking the time-series envelope.
The exact same data pings and modeling environment used to create the ambiguity
surfaces in Fig. 3 were used with Eq. (2) to make new ambiguity surfaces and these are
shown in the top panels of Fig. 4. The source is correctly localized at both .78-km and
2-km ranges. Focalization was not used (i.e. the environment was fixed for computing the
430
M. SIDERIUS ET AL.
Depth (m)
1
20
40
40
60
60
80
.75
100
80
.75
100
0
0.5
1
1.5
Range (km)
2
1
1
20
Depth (m)
1
20
2
2.5
Range (km)
3
1
20
40
40
60
60
80
1.5
80
.75
100
.75
100
4
4.5
5
5.5
Range (km)
6
9
9.5
10
10.5
Range (km)
11
Figure 4. Source localization ambiguity surfaces for ASCOT-01 data taken from 4 source-receiver
ranges using Eq. (2). In the top left panel the source is at about 0.78 km, top right about 1.85 km,
bottom left about 5.03 km and bottom right about 9.76 km (depth for all is about 98 m).
replicas). Note, that in Fig. 4 a larger search range of possible source locations is used (to
see if false peaks were nearby) and the color scale is smaller. The replicas for correlating
were generated using the BELLHOP propagation model. The ASCOT-01 environment is
probably not an unusual one. Similar observations about robust features of the impulse
response have previously been made at different sites and correlation functions of the
envelope data were used to estimate source location [12].
The envelope correlation was further tested using longer range data. In the bottom
panels of Fig. 4, data is taken with the source about 5 and 10 km from the VLA. In both
cases the source is localized correctly.
The localization results shown so far have been taken from relatively low frequency
data (200–800 Hz). This band is very relevant to the study of passive sonar applications.
However, many active sonar applications extend to frequencies well above this. It is
a difficult task to apply standard MFP localization to higher frequency data. That is
because accurate modeling is more difficult at higher frequency since the relevant time
and length scales of the environment get smaller and require finer sampling. However,
observing the arrival patterns on the VLA from data taken centered around 1.2 kHz
shows similar arrival features as seen with the lower frequency data. Further, using the
BELLHOP ray propagation model, the computation time is about the same as for the low
frequency band. Localization results using the envelope of 1.2 kHz (center frequency of
1-s LFM) data collected at 5 km is shown in Fig. 5. As with the low frequency data, the
source is localized correctly. Note, that as before no focalization or optimization on the
environment or measurement geometry was applied to the processing.
For the localizations previously shown, the source waveform was assumed known
and therefore a matched-filter could be applied. This is a reasonable assumption for
applications such as geo-acoustic inversion or active sonar. In other applications such
431
SOURCE LOCALIZATION IN A VARIABLE ENVIRONMENT
Depth (m)
1
20
0.95
40
0.9
60
0.85
80
0.8
100
0.75
4
5
Range (km)
6
Figure 5. Source localization ambiguity surface for 1.2 kHz (center frequency) ASCOT-01 data
using Eq. (2). Results are for the source located about 5.03 km away.
20
0.95
40
0.9
60
0.85
80
0.8
100
0.75
4
5
Range (km)
6
1
20
Depth (m)
Depth (m)
1
0.95
40
60
0.9
80
100
0.85
4
5
Range (km)
6
Figure 6. Ambiguity surface for 5.03 km data match-filtered with a single beam time series (200–
800 Hz LFM). Color scale for left panel is the same as for Fig. 4 and is decreased slightly on
right.
as passive sonar, the source waveform will likely be unknown. Even without prior
knowledge of the source waveform, there are several ways to use the envelope correlation
for localization (or possibly geoacoustic inversion from sources of opportunity). The
envelope correlation uses the relative timing and amplitudes of the multipath arrivals
which implies that receptions must be broad-band and pulse compressed. One way to
achieve the pulse compression is to auto-correlate or cross-correlate the hydrophone data
and compare this with the replica fields processed in a similar way. The auto- or crosscorrelations provide pulse compression but introduce additional peaks that appear like
multipath. This may introduce some ambiguities but these may not degrade the results
since similar features are reproduced with the replica data. This type of processing has
been used successfully on hydrophone data taken from a highly sparse array [13].
Another approach to estimating the source waveform is to take advantage of the
vertical array and planewave beamform. Taking individual beams will eliminate (or
reduce) the multipath interference and the beam time series provides an estimate of the
source transmit waveform. This can then be used as the matched-filter and the VLA data
can be processed for a pulse-compressed (impulse-like) response. Using the beamforming
approach to estimating the source waveform produced an estimated matched-filter arrival
pattern that was nearly identical to that using the true matched-filter. In Fig. 6 source
localization is shown for this data using exactly the same replica fields as for the bottom
left panel of Fig. 4.
432
5
M. SIDERIUS ET AL.
Discussion and conclusion
While standard MFP processing was successful in the Strait of Sicily, it failed to localize a
source just 2 km away for a site off the New England coast. The failure is likely due to the
multipath arrivals that are strongly affected by the spatial variability of the ocean. It is not
practical (or even possible) to include this variability in the propagation modeling needed
for MFP. An alternative process is described that achieves greater stability by correlating
the time series envelope. Results presented show correct localization out to 10 km even at
frequencies above 1 kHz. Further, this process was shown to be successful at localization
even when no prior knowledge of the source transmit waveform was assumed.
Acknowledgments
This work was initiated to support the SACLANTCEN Programme of Work. The analysis
was supported by ONR.
References
1. Baggeroer, A.B., Kuperman, W.A. and Mikhalevsky, P.N., An overview of matched field
methods in ocean acoustics, IEEE J. Ocean Eng. 18, 401–424 (1993).
2. Tolstoy, A., Matched-Field Processing for Underwater Acoustics (World Scientific, Singapore,
1993).
3. Collins, M.D. and Kuperman, W.A., Focalization: Environmental focusing and source localization, J. Acoust. Soc. Am. 90, 1410–1422 (1991).
4. Sellschopp, J., Siderius, M. and Nielsen, P., Advent’99 pre-processed acoustic and environmental cruise data. CD-35, SACLANT Undersea Research Centre, La Spezia, Italy
(2000).
5. Siderius, M., Nielsen, P., Sellschopp, J., Snellen, M. and Simons, D., Experimental study of
geo-acoustic inversion uncertainty due to ocean sound-speed fluctuations, J. Acoust. Soc.
Am. 110, 769–781 (2001).
6. Nielsen, P., Siderius, M. and Sellschopp J., Broadband acoustic signal variability in two “typical” shallow-water regions. In Impact of Littoral Environmental Variability on Acoustic
Predictions and Sonar Performance, edited by N.G. Pace and F.B. Jensen (Kluwer, The
Netherlands, 2002) pp. 237–244.
7. Soares, C., Siderius, M. and Jesus, S., Source localization in a time-varying ocean waveguide,
J. Acoust. Soc. Am. (to appear 2002).
8. Jensen, F.B. and Ferla, M.C., SNAP: The SACLANTCEN normal-mode acoustic propagation
model. Report SM-121, SACLANT Undersea Research Centre, La Spezia, Italy (1979).
9. Gerstoft, P., SAGA Users manual 2.0. An inversion software package. Report SM-333,
SACLANT Undersea Research Centre, La Spezia, Italy (1997).
10. Porter, M.B., The BELLHOP ray/beam acoustic propagation model. http://oalib.saic.com
11. Sellschopp, J., Nielsen, P. and Siderius, M., Combination of acoustics with high resolution
oceanography. In Impact of Littoral Environmental Variability on Acoustic Predictions
and Sonar Performance, edited by N.G. Pace and F.B. Jensen (Kluwer, The Netherlands,
2002) pp. 19–26.
12. Porter, M.B., Jesus, S., Stephan, Y., Demoulin, X. and Coelho, E., Exploiting reliable features
of the ocean channel response. In Shallow Water Acoustics, edited by by R. Zhang and J.
Zhou (China Ocean Press, Beijing, 1997).
13. Porter, M.B, Hursky, P. and Tiemann, C.O., Model-based tracking for autonomous arrays. In
Proc. MTS/IEEE Oceans 2001, 786–792 (2001).