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Nonlinear optimization of autonomous undersea vehicle sampling strategies for oceanographic data‐assimilation
Author(s) -
Heaney Kevin D.,
Gawarkiewicz Glen,
Duda Timothy F.,
Lermusiaux Pierre F. J.
Publication year - 2007
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.20183
Subject(s) - suite , weighting , data assimilation , software deployment , computer science , sampling (signal processing) , nonlinear system , scalar (mathematics) , sensitivity (control systems) , genetic algorithm , environmental science , meteorology , marine engineering , mathematical optimization , engineering , geography , mathematics , machine learning , telecommunications , medicine , physics , geometry , archaeology , quantum mechanics , electronic engineering , detector , radiology , operating system
The problem of how to optimally deploy a suite of sensors to estimate the oceanographic environment is addressed. An optimal way to estimate (nowcast) and predict (forecast) the ocean environment is to assimilate measurements from dynamic and uncertain regions into a dynamical ocean model. In order to determine the sensor deployment strategy that optimally samples the regions of uncertainty, a Genetic Algorithm (GA) approach is presented. The scalar cost function is defined as a weighted combination of a sensor suite's sampling of the ocean variability, ocean dynamics, transmission loss sensitivity, modeled temperature uncertainty (and others). The benefit of the GA approach is that the user can determine “optimal” via a weighting of constituent cost functions, which can include ocean dynamics, acoustics, cost, time, etc. A numerical example with three gliders, two powered AUVs, and three moorings is presented to illustrate the optimization approach in the complex shelfbreak region south of New England. © 2007 Wiley Periodicals, Inc.