Ritter, Tobias ; Euler, Juliane ; Ulbrich, Stefan ; Stryk, Oskar von (2016)
Decentralized Dynamic Data-Driven Monitoring of Atmospheric Dispersion Processes.
In: Procedia Computer Science, 80
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 ; Euler, Juliane ; Ulbrich, Stefan ; Stryk, Oskar von |
| Type of entry: | Bibliographie |
| Title: | Decentralized Dynamic Data-Driven Monitoring of Atmospheric Dispersion Processes |
| Language: | German |
| Date: | 2016 |
| Journal or Publication Title: | Procedia Computer Science |
| Volume of the journal: | 80 |
| URL / URN: | http://www.sciencedirect.com/science/article/pii/S1877050916... |
| Corresponding Links: | |
| 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. |
| Uncontrolled Keywords: | Cooperative Vehicle Controller |
| Additional Information: | International Conference on Computational Science 2016, {ICCS} 2016, 6-8 June 2016, San Diego, California, {USA} |
| 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 |
| Last Modified: | 18 Mar 2019 10:17 |
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