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Genetic Machine Learning Approach for Link Quality Prediction in Mobile Wireless Sensor Networks Read chapter Read first chapter

2014 | OriginalPaper | Chapter

4. Real-Time Modeling of Ocean Currents for Navigating Underwater Glider Sensing Networks

Authors : Dongsik Chang, Xiaolin Liang, Wencen Wu, Catherine R. Edwards, Fumin Zhang

Published in: Cooperative Robots and Sensor Networks

Publisher: Springer Berlin Heidelberg

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Abstract

Ocean models that are able to provide accurate and real-time prediction of ocean currents will improve the performance of glider navigation. In this paper, we propose a novel approach to compute a model for ocean currents at higher resolution than existing approaches. By focusing on a small area and incorporating measurements from multiple gliders, we are able to perform real-time computation of the model, which can be used to improve performance of underwater glider navigation in the ocean. Our model uses a lower resolution, larger scale dataset generated from existing models to initialize the computation. We have also demonstrated incorporating data streams from high frequency (HF) radar measurements of surface currents. Glider navigation performance using the proposed ocean currents model is demonstrated in a simulated flow field based on data collected off the coast of Georgia, USA.

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previous chapter Localization, Route Planning, and Smartphone Interface for Indoor Navigation
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Metadata
Title
Real-Time Modeling of Ocean Currents for Navigating Underwater Glider Sensing Networks
Authors
Dongsik Chang
Xiaolin Liang
Wencen Wu
Catherine R. Edwards
Fumin Zhang
Copyright Year
2014
Publisher
Springer Berlin Heidelberg
Book
Cooperative Robots and Sensor Networks

Print ISBN: 978-3-642-39300-6

Electronic ISBN: 978-3-642-39301-3

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-642-39301-3_4

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