Acoustically focused adaptive sampling and on-board routing for marine rapid environmental assessment

• Published in: Journal of marine systems 2009-11, Vol.78, p.S393-S407
• Main Authors: Wang, Ding, Lermusiaux, Pierre F.J., Haley, Patrick J., Eickstedt, Donald, Leslie, Wayne G., Schmidt, Henrik
• Format: Article
• Language: English
• Subjects: Acoustic propagation; Acoustics; Adaptive sampling; AOSN; Autonomous Underwater Vehicle; AUV; Computer networks; Data assimilation; Decision-making under uncertainty; Error subspace; ESSE; Feature tracking; Marine; Mathematical models; Methodology; Ocean modeling; Ocean observing system; Oceans; Optimization; Path planning; Pianosa island; Primitive-equations; Sampling; Tyrrhenian sea; Vehicle routing
• ISSN: 0924-7963; 1879-1573
• 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.