Ritter, Tobias ; Euler, Juliane ; Ulbrich, Stefan ; Stryk, Oskar von (2016)
Decentralized Dynamic Data-Driven Monitoring of Atmospheric Dispersion Processes.
In: Procedia Computer Science, 80
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Ritter, Tobias ; Euler, Juliane ; Ulbrich, Stefan ; Stryk, Oskar von |
| Art des Eintrags: | Bibliographie |
| Titel: | Decentralized Dynamic Data-Driven Monitoring of Atmospheric Dispersion Processes |
| Sprache: | Deutsch |
| Publikationsjahr: | 2016 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia Computer Science |
| Jahrgang/Volume einer Zeitschrift: | 80 |
| URL / URN: | http://www.sciencedirect.com/science/article/pii/S1877050916... |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Cooperative Vehicle Controller |
| Zusätzliche Informationen: | International Conference on Computational Science 2016, {ICCS} 2016, 6-8 June 2016, San Diego, California, {USA} |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Simulation, Systemoptimierung und Robotik Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) 04 Fachbereich Mathematik 04 Fachbereich Mathematik > Optimierung 04 Fachbereich Mathematik > Optimierung > Nonlinear Optimization |
| Hinterlegungsdatum: | 08 Sep 2016 10:19 |
| Letzte Änderung: | 18 Mär 2019 10:17 |
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