@article{wang2009,
title = {Acoustically Focused Adaptive Sampling and On-Board Routing for Marine Rapid
Environmental Assessment},
author = {Ding Wang and Pierre F.J Lermusiaux and Patrick J. Haley and Donald Eickstedt
and Wayne G. Leslie and Henrik Schmidt},
journal = {Journal of Marine Systems},
pages = {S393 - S407},
volume = {78, Supplement},
year = {2009},
abstract = {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.}}