Wang, D., P.F.J. Lermusiaux, P.J. Haley, D. Eickstedt, W.G. Leslie and H. Schmidt, 2009. Acoustically Focused Adaptive Sampling and On-board Routing for Marine Rapid Environmental Assessment. Special issue of Journal of Marine Systems on "Coastal processes: challenges for monitoring and prediction", Drs. J.W. Book, Prof. M. Orlic and Michel Rixen (Guest Eds), 78, S393-S407, doi: 10.1016/j.jmarsys.2009.01.037.
Variabilities in the coastal ocean environment span a wide range of spatial and temporal scales. From an
acoustic viewpoint, the limited oceanographic measurements and today’s ocean computational capabilities
are not always able to provide oceanic-acoustic predictions in high-resolution and with enough accuracy.
Adaptive Rapid Environmental Assessment (AREA) is an adaptive sampling concept being developed in
connection with the emergence of Autonomous Ocean Sampling Networks and interdisciplinary ensemble
predictions and adaptive sampling via Error Subspace Statistical Estimation (ESSE). By adaptively and
optimally deploying in situ sampling resources and assimilating these data into coupled nested ocean and
acoustic models, AREA can dramatically improve the estimation of ocean fields that matter for acoustic
predictions. These concepts are outlined and a methodology is developed and illustrated based on the
Focused Acoustic Forecasting-05 (FAF05) exercise in the northern Tyrrhenian sea. The methodology first
couples the data-assimilative environmental and acoustic propagation ensemble modeling. An adaptive
sampling plan is then predicted, using the uncertainty of the acoustic predictions as input to an optimization
scheme which finds the parameter values of autonomous sampling behaviors that optimally reduce this
forecast of the acoustic uncertainty. To compute this reduction, the expected statistics of unknown data to be
sampled by different candidate sampling behaviors are assimilated. The predicted-optimal parameter values
are then fed to the sampling vehicles. A second adaptation of these parameters is ultimately carried out in the
water by the sampling vehicles using onboard routing, in response to the real ocean data that they acquire.
The autonomy architecture and algorithms used to implement this methodology are also described. Results
from a number of real-time AREA simulations using data collected during the Focused Acoustic Forecasting
(FAF05) exercise are presented and discussed for the case of a single Autonomous Underwater Vehicle (AUV).
For FAF05, the main AREA-ESSE application was the optimal tracking of the ocean thermocline based on
ocean-acoustic ensemble prediction, adaptive sampling plans for vertical Yo-Yo behaviors and subsequent
onboard Yo-Yo routing.