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Decentralized Dynamic Data-Driven Monitoring of Atmospheric Dispersion Processes

Ritter, Tobias and Euler, Juliane and Ulbrich, Stefan and Stryk, Oskar von (2016):
Decentralized Dynamic Data-Driven Monitoring of Atmospheric Dispersion Processes.
In: Procedia Computer Science, pp. 919 - 930, 80, ISSN 1877-0509,
[Online-Edition: http://www.sciencedirect.com/science/article/pii/S1877050916...],
[Article]

Abstract

Online state and parameter estimation of atmospheric dispersion processes using multiple mobile sensor platforms is a prominent example of Dynamic Data-Driven Application Systems (DDDAS). Based on repeated predictions of a partial differential equation (PDE) model and measurements of the sensor network, estimates are updated and sensor trajectories are adapted to obtain more informative measurements. While most of the monitoring strategies require a central supercomputer, a novel decentralized plume monitoring approach is proposed in this paper. It combines the benefits of distributed approaches like scalability and robustness with the prediction ability of {PDE} process models. The strategy comprises model order reduction to keep calculations computationally tractable and a joint Kalman Filter with Covariance Intersection for incorporating measurements and propagating state information in the sensor network. Moreover, a cooperative vehicle controller is employed to guide the sensor vehicles to dynamically updated target locations that are based on the current estimated error variance.

Item Type: Article
Erschienen: 2016
Creators: Ritter, Tobias and Euler, Juliane and Ulbrich, Stefan and Stryk, Oskar von
Title: Decentralized Dynamic Data-Driven Monitoring of Atmospheric Dispersion Processes
Language: German
Abstract:

Online state and parameter estimation of atmospheric dispersion processes using multiple mobile sensor platforms is a prominent example of Dynamic Data-Driven Application Systems (DDDAS). Based on repeated predictions of a partial differential equation (PDE) model and measurements of the sensor network, estimates are updated and sensor trajectories are adapted to obtain more informative measurements. While most of the monitoring strategies require a central supercomputer, a novel decentralized plume monitoring approach is proposed in this paper. It combines the benefits of distributed approaches like scalability and robustness with the prediction ability of {PDE} process models. The strategy comprises model order reduction to keep calculations computationally tractable and a joint Kalman Filter with Covariance Intersection for incorporating measurements and propagating state information in the sensor network. Moreover, a cooperative vehicle controller is employed to guide the sensor vehicles to dynamically updated target locations that are based on the current estimated error variance.

Journal or Publication Title: Procedia Computer Science
Volume: 80
Uncontrolled Keywords: Cooperative Vehicle Controller
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Simulation, Systems Optimization and Robotics Group
Exzellenzinitiative
Exzellenzinitiative > Graduate Schools
Exzellenzinitiative > Graduate Schools > Graduate School of Computational Engineering (CE)
04 Department of Mathematics
04 Department of Mathematics > Optimization
04 Department of Mathematics > Optimization > Nonlinear Optimization
Date Deposited: 08 Sep 2016 10:19
Official URL: http://www.sciencedirect.com/science/article/pii/S1877050916...
Additional Information:

International Conference on Computational Science 2016, {ICCS} 2016, 6-8 June 2016, San Diego, California, {USA}

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