Robust, Distributed Estimation of Internal Wave Parameters via Inter-Drogue Measurements

Internal waves are important to oceanographers because, as they travel, they are capable of displacing mass, such as plankton and small fish. This paper considers a group of drogues estimating the physical parameters that determine the dynamics of an ocean linear internal wave. While underwater, ind...

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Bibliographic Details
Published in:IEEE transactions on control systems technology Vol. 22; no. 3; pp. 980 - 994
Main Authors: Ouimet, Michael, Cortes, Jorge
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Construction
Cooperative parameter estimation
data fusion
Derivatives
Dynamics
Estimates
Estimating
Internal waves
Lagrangian dynamics
Marine
Noise measurement
ocean linear internal waves
Oceans
Propagation
Robustness
robustness to error
Sea measurements
Sea surface
Strategy
Surface waves
underwater robotic drifters
data fusion
Lagrangian dynamics
underwater robotic drifters
robustness to error
Cooperative parameter estimation
ocean linear internal waves
ISSN:1063-6536, 1558-0865
Online Access:Get full text
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Summary:Internal waves are important to oceanographers because, as they travel, they are capable of displacing mass, such as plankton and small fish. This paper considers a group of drogues estimating the physical parameters that determine the dynamics of an ocean linear internal wave. While underwater, individual drogues do not have access to absolute position information and can only rely on inter-drogue measurements. Building on this data and the knowledge of the drogue dynamics under the flow induced by the internal wave, we propose the Vanishing Distance Derivative Detection Strategy to allow individual drogues to determine the wave parameters. We analyze the correctness and robustness of this strategy under noiseless and noisy measurements, respectively. We also introduce a general methodology, termed pth-Order Parameter Fusion, for combining the parameter estimates obtained at different times and characterize its error. Simulations illustrate our results.
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ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2013.2270952