AREA: ADAPTIVE RAPID ENVIRONMENTAL ASSESSMENT
HENRIK SCHMIDT
Massachusetts Institute of Technology, Cambridge, MA 02139, USA
E-mail: [email protected]
AREA: Adaptive Rapid Environmental Assessment is a new operational paradigm for
minimizing the sonar performance uncertainty in shallow water. The coastal environment is characterized by variability on small spatial scales and short temporal scales,
which obstruct the formation of a robust and reliable tactical picture. Thus, a Rapid Environmental Assessment (REA) capability has long been recognized as a tactical need,
but its implementation is being constrained by limited in-situ measurement resources.
Ocean modeling and data assimilation can produce 4-D field estimates together with
their associated uncertainty. However, the resolution is inadequate for direct use in
acoustic environment prediction. On the other hand the forecasts can be used to identify
features such as fronts and eddies which are critical to the acoustic sonar performance
uncertainty and which should therefore be targeted by the REA resources. AREA uses
environmental acoustic and sonar models to translate the oceanographic and geophysical parameter uncertainty estimates into PDFs for the appropriate sonar performance
metric. These are then used to objectively design survey patterns that target regions
and parameters that produce the best possible sonar performance prediction within the
actual operational constraints.
1 Introduction
The uncertainty of the acoustic predictability is critical to the dB-budget of classical sonar
systems by directly affecting the detection and false alarm probabilities. The uncertainty
of the acoustic environment prediction is also one of the major obstacles to adapting new
model-based sonar processing frameworks, such as matched field processing (MFP) >dH,
to the coastal environment.
The acoustic uncertainty associated with spatially and temporarily varying sound speed
and the random characteristics of the bottom are also of critical influence to acoustic
communication systems, which with the integration of new Autonomous Ocean Sampling
Network (AOSN) >1H concept in the operational Navy is becoming of increasingly tactical
significance. The prediction of the fidelity of the communications link is important for
modern adaptive platform behaviors, where a manned or unmanned submersible may seek
an optimal depth for using its acoustic communication systems.
The performance prediction of such acoustic systems is dependent on estimates of the
statistics of the environmental acoustic parameters. In general, the best we can hope for
is knowledge of the second order statistics such as the auto-correlation functions of the
sound speed distribution, the bottom density and the irregular bottom profile; as well as
the cross correlation functions between these parameters.
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N.G. Pace and F.B. Jensen (eds.), Impact of Littoral Environmental Variability on Acoustic Predictions and
Sonar Performance, 587-594.
© 2002 Kluwer Academic Publishers. Printed in the Netherlands.
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Sonar
Prediction
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Resolution
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Figure 1. Sonar system performance is dependent on acoustic environment variability over a wide
range of scales. Optimal environmental assessment will therefore be a compromise between conflicting requirements of coverage and resolution. By targeting areas of high sensitivity to the sonar
system, AREA will shift the deterministic assessment towards smaller scales.
2
Sonar environment variability
The coastal environment has variability over a wide range of spatial and temporal scales.
Therefore, the environmental assessment is facing the classical conflict between resolution, needed to capture the fine scale variability and coverage, needed for the large scale
environmental phenomena. Thus, the resources available must be focusing on the scales
critical to the specific sonar system, but these may also span a wide range of scales,
requiring that either resolution or coverage be sacrificed.
Of particular importance to the sonar detection statistics is the environmental scales
centered at the acoustic wavelengths of the sonar systems. Thus, as illustrated in Fig. 1,
variability on spatial scales of a few meters may be critical to the acoustic predictability. Smaller scale variability is averaged out by the acoustic wavelength, while larger
scale variability can be accurately assessed using satellite remote sensing and traditional
oceanographic surveys.
To determine the critical intermediate scales of the environmental variability an insitu measurement capability has long been recognized as a tactical need. However, its
implementation is being constrained by limited resources. Thus, as illustrated in Fig. 1, the
compromise between the measurement resolution and the coverage necessarily sacrifices
both small and large scale variability of significance. Consequently the limited resources
will always have the effect that the ocean environment will be under-sampled, both
in terms of coverage and resolution, eliminating the possibility of a true deterministic
predictability.
The lower limit of the spatial scales that can be described deterministically depends on
the coupling to the acoustic environment and the particular sonar configuration. Thus, for
example, it may be a waste to use valuable REA resources on assessing small scale spatial
variability of the seabed during the morning where a well developed surface duct exists.
ADAPTIVE RAPID ENVIRONMENTAL ASSESSMENT
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On the other hand such measurements may significantly reduce the prediction uncertainty
later in the day, where the surface duct disappears and strong bottom interaction becomes
the dominant environmental condition (’afternoon effect’).
Oceanographic forecasting by modeling and data assimilation can produce 4-D
oceanographic field estimates and their associated uncertainties [3, 4]. However, even
though highly applicable to a wide range of applications such as coastal environmental
management, the uncertainty of the oceanographic forecast is in general inadequate for
direct use in acoustic environment prediction frameworks.
Most importantly the spatial and temporal grids are limited by the available computational resources. Even using nested computational grids, spatial scale smaller than several
hundred meters in the horizontal, and tens of meters in the vertical cannot be modeled
deterministically, as indicated in Fig. 1. Modern modeling and assimilation frameworks
have a capability of representing the smaller, sub-grid-scale variability statistically. However, in general these scales are at least an order of magnitude larger than the scales
important to the acoustic predictability. Another reason for the inadequacy of the ocean
forecasts for sonar performance prediction is the highly sensitive and non-linear relation
between the ocean variability and the acoustic environment statistics [5]. This issue is obviously enhanced by the limits in scale imposed by computational constraints. The limited
availability of local data for assimilation into the modeling framework severely limits the
usefulness of the forecasts to the acoustic environment prediction. New adaptive sampling
concepts based on previous forecasts are currently being developed in connection with the
emergence of the new AOSN technology [6]. In principle these could be used to deploy
the limited tactical resources in a manner which is optimal to the acoustic forecasting [3].
However, the time required to generate the forecasts, design the adaptive sampling patterns, and subsequently assimilate the new data to produce accurate now-casts is orders of
magnitude larger than the temporal scales of the littoral ocean. Thus, the computational
resources will remain insufficient for the ocean forecasting frameworks to be directly applicable to operational prediction of the acoustic environment without supporting in-situ
measurements.
In spite of its limited resolution, ocean forecasting can be extremely useful for providing large-scale coverage, and for identifying region and features with strong variability,
such as coastal fronts, which could be targeted by the REA resources. As such ocean
forecasting frameworks such as HOPS [4] are cornerstones of the AREA concept, allowing the environmental assessment resources to be deployed in regions of high variability
where resolution is crucial, without sacrificing coverage.
Using the ocean forecasts to define optimal deployment strategies for the REA resources, the limit of deterministic characterization may be shifted significantly towards
smaller scales. This can actually produce much finer resolution than earlier achieved by
the same REA resources because the forecasting framework is providing the coverage, as
illustrated in Fig. 1.
Another environmental factor particular to the littoral environment is the significance
of the seabed because of the typical downward refracting sound speed profile. Thus, for
many sonar scenarios, the spatial variability of the seabed, i.e. roughness and volume
inhomogeneities, is far more severe to the acoustic variability than the oceanographic
uncertainty. Clearly, this will affect the optimal REA resource allocation. Thus, for
example, one available AUV may be more optimally deployed for side-scan/sub-bottom
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Oceanography
Ocean
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HOPS
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Acoustic
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G&G
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OA
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Geoacoustic
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AREA
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PDFs
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Figure 2. AREA functionality. Fore- and now-casts of the local oceanography and geology are
producing spatial and temporal environmental statistics in the form of coupled PDFs of the associated environmental acoustics parameters, subsequently translated toPDFs for the sonar performance.
These PDFs then form the basis for optimal deployment of the in-situ REA resources.
profiling than for water column sampling.
Adaptive Rapid Environmental Assessment (AREA) is a probabilistic approach to the
adaptive sampling problem of littoral REA. By combining the coverage of coastal ocean
forecasting frameworks with adaptive deployment of in-situ measurement resources for
high-resolution measurements, AREA is envisioned as a real time tactical tool for not
only capturing, but also minimizing the acoustic uncertainty of significance to specific
sonar systems.
The AREA framework can also be used to objectively evaluate the performance of
new REA concepts, such as e.g Acoustically Focused Ocean Sampling (AFOS) [6] and
Acoustic Data Assimilation (ADA) [7] recently developed at MIT. In contrast to Ocean
Acoustic Tomography [8], ADA does not require a substantial acoustic network, but
allows for even simple point-to-point acoustic transmissions to be assimilated consistently
with other oceanographic data, and may therefore become a valuable REA resource,
directly reflecting the acoustic uncertainty [7].
3
AREA: Adaptive Rapid Environmental Assessment
The AREA concept is envisioned as an optimal combination of classical environmental
assessment based on databases and local measurements, and full-blown forecasting frameworks based on modeling and assimilation with adaptive in-situ sampling. As described
above, both of these approaches are resource limited and do not capture the acoustic
environment optimally in terms of acoustic uncertainty. AREA instead uses the modeling
and assimilation framework to continuously provide an initial estimate of the oceanographic fields and their coupled uncertainties, and their significance to the sonar system
dB-budget. These forecasts are then used to identify optimal deployment patterns for the
in-situ sampling resources such as XBT, CTD casts and AUV surveys. The functionality
ADAPTIVE RAPID ENVIRONMENTAL ASSESSMENT
a)
591
b)
Figure 3. Coastal forecasting during GOATS/MEANS’2000 experiment. A nested implementation
of HOPS was used to forecast the temperature field in Procchio Bay, Elba, Italy in October 2000.
This field estimate and the associated estimated temperature uncertainty is used to test the AREA
concept for optimizing the performance prediction of the transmission loss associated with the
insonification a very shallow beach area from an off-shore sonar platform.
of AREA is illustrated in Fig. 2, and is envisioned to proceed as follows
An environmental forecasting framework based on assimilating data from databases,
satellite remote sensing and the most recent local REA data to provide estimates of
the oceanographic and geophysical fields, e.g. parameterized as Empirical Orthogonal
Functions (EOF), and their associated uncertainties. The EOF parameterization also
directly characterizes the spatial and temporal scales of the environmental features, needed
later for the adaptive objective analysis. Using combined acoustic propagation and sonar
models the system sensitivity is then determined, using quantitative sensitivity measures
such as the Fisher information matrix, directly quantifying the sonar uncertainty in terms
of the uncertainties of the oceanographic parameters [5]. This step produces a sonar
performance metric in the form of a sonar PDFs, based on, and consistent with the
environmental forecasts.
The sonar PDFs are still in the form of ’uncertainty maps’ over the ocean volume,
but representing the ultimate effect on the sonar detection dB-budget. The sonar uncertainty PDFs will then be combined with an objective measure of cost associated with
the REA resource deployment, to provide a conditional probability distribution (CPD)
which directly identifies which environmental parameters must be targeted to minimize
the uncertainty of the acoustic prediction within the operational constraints.
This initial CPD is inherently assuming the underlying environmental uncertainty to
have Gaussian statistics, an assumption many years of sonar operation experience shows to
be unrealistic. To optimize the deployment pattern accordingly, Monte Carlo simulation
of the sonar performance is performed. First, the new adaptive sampling patterns are
applied to ’virtual oceans’ produced by generating random ocean realizations based on
the forecast field and error estimates. These are then translated into sonar PDFs applying
the environmental acoustic modeling framework. In case the associated sonar performance
uncertainty is unsatisfactory, the deployment strategy may be changed, and the procedure
repeated. Finally, after the REA measurements have been collected, they are objectively
analyzed similarly to the ’virtual’ data, using the forecast scales, and passed through the
sonar modeling framework, producing sonar performance predictions with significantly
sharper uncertainty.
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H. SCHMIDT
a)
b)
c)
d)
Figure 4. Transmission loss estimate for Procchio Bay track obtained by objective analysis of simulated surface temperature measurements using spatial statistics predicted by HOPS model, followed
by Monte Carlo simulation using PE acoustic propagation model. (a) Temperature estimate along
communication track. (b) Estimated uncertainty of temperature field. (c) Contours of estimated
mean transmission loss in dB. (d) Estimated uncertainty of transmission loss.
4
AREA example
As an example of the functionality of AREA for reducing the uncertainty of acoustic performance prediction, a low-frequency mine-countermeasure sonar scenario is used. The
SACLANTCEN GOATS JRP has as one of its objective to investigate the performance
of new low-frequency, bi- and multi-static sonar concepts, using a powerful sonar for
insonifying the very shallow water near the beach from a position off-shore, with multiple receivers being caried on one or more AUVs operating in the target area as bi-static
platforms [9]. During the SACLANTCEN GOATS’2000 experiment [10] carried out in
collaboration with MIT and Harvard University, among others, the Harvard Ocean Prediction System (HOPS) was used to forecast the temperature and current field in the
bay area which was the focus of the bi-static MCM experiments. Figure 3(a) shows an
example of the temperature estimate at 10 m depth. Figure 3(b) shows a hypothetical
insonification path from an off-shore 3.5 kHz source pointed towards shore in Procchio
Bay.
First the HOPS forecast is used to generate a ’true ocean’ by generating a realization
of the forecast statistics. This ’virtual ocean’ is then assumed to be ’sampled’ by an AUV
using different survey patterns.
In the first example, the AUV is assumed to perform a constant-depth survey close
ADAPTIVE RAPID ENVIRONMENTAL ASSESSMENT
a)
b)
c)
d)
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Figure 5. Transmission loss estimate for Procchio Bay track obtained by objective analysis of simulated volume temperature measurements by an AUV performing a ’yoyo’ survey pattern, using
spatial statistics predicted by HOPS model, followed by Monte Carlo simulation using PE acoustic
propagation model. (a) Temperature estimate along communication track. (b) Estimated uncertainty of temperature field. (c) Contours of estimated mean transmission loss in dB. (d) Estimated
uncertainty of transmission loss.
to the sea surface, measuring the temperature. These measurements are then objectively
analysed using the spatial statistics produced by HOPS to produce the estimate of the
sound speed shown in Fig. 4(a), and the associated uncertainty estimate in Fig. 4(b).
As expected the sound speed has small uncertainty close to the sea surface where the
temperature was measured directly by the AUV, while a large uncertainty is present at
depths beyond 10 m, where no direct measurements are made by the AUV.
Using this new sound speed estimate and uncertainty, the expected transmission loss
vs range and depth in Fig. 4(c) is obtained using Monte-Carlo simulation, based on
40 realizations, and the associated uncertainty in dB shown in Fig. 4(d). As is clear
from Fig. 4(d) the transmission loss predicted for a communication source off-shore to a
receiver close to the beach is of order 10 dB. Consequently, the bistatic sonar would be
associated with 10–20 dB uncertainty, severely affecting the detection statistics.
Figure 5 shows the corresponding sound speed and transmission loss estimates in
the case where one of the AUVs is performing a ’yoyo’ survey on its path to the target
area, reducing the transmission loss uncertainty everywhere in the water column to less
than 1 dB. Even though this hypothetical scenario ignores the temporal variability, it
clearly illustrates how the sonar performance prediction may be significantly improved
by optimally deploying the resources aavailable for environmental assessment.
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Conclusion
AREA is a new operational paradigm currently being developed by MIT in a partnership
with other research teams involved in the ONR ’Capturing Uncertainty’ DRI. Oceanographic forecasts obtained by modeling and assimilation networks such as HOPS are
used to provide initial estimates of the environment and its spatial and temporal statistics.
Acoustic propagation and sonar performance models are then used to derive the associated PDFs for the performance of the acoustic systems, and in turn used to identify the
optimal deployment patterns for the available rapid environmental assessment resources.
This paper has demonstrated the concept applied to establishing an accurate estimate
of the transmission loss associated with a littoral mine countermeasures scenario, but is
equally applicable to modern high-resolution passive sonar processing techniques such as
matched field processing, which are even more sensitive to environmental mismatch.
Acknowledgments
The author appreciates the effort of the HOPS group at Harvard for providing the forecasts
of the oceanographic field estimates used for the development of AREA. This research
is sponsored by the Office of Naval Research under the Capturing Uncertainty DRI.
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