Shutin, Dmitriy und Wiedemann, Thomas und Hinsen, Patrick (2023) Distributed Superresolution Gas Source Localization based on Poisson Equation. In: 9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2023-12-10 - 2023-12-13, Costa Rica. doi: 10.1109/camsap58249.2023.10403503. ISBN 979-835034452-3.
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Kurzfassung
Accurate modeling and estimation of airborne material in Chemical, Biological, Radiological, or Nuclear accidents are vital for effective disaster response. In this paper a method that combines prior domain knowledge in terms of Partial Differential Equations (PDEs), sparse Bayesian learning (SBL), and cooperative estimation for multiple robots or sensor networks is proposed to identify the number and locations of gas sources. Using method of Green’s functions and the adjoint state method, a gradient-based optimization with respect to source location is derived, allowing superresolving (arbitrary) source locations. By combing the latter with SBL, a sparse source support can be identified, thus indirectly assessing the number of sources. Both steps are computed cooperatively, utilizing the agent network to share information. Simulation results demonstrate the effectiveness of the approach.
elib-URL des Eintrags: | https://elib.dlr.de/199996/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||||||
Titel: | Distributed Superresolution Gas Source Localization based on Poisson Equation | ||||||||||||||||
Autoren: |
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Datum: | 10 Dezember 2023 | ||||||||||||||||
Erschienen in: | 9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Nein | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||
DOI: | 10.1109/camsap58249.2023.10403503 | ||||||||||||||||
ISBN: | 979-835034452-3 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | Sparse Bayesian learning, Super-res0olution, Partial differential equations, Poisson equation, gas source localization | ||||||||||||||||
Veranstaltungstitel: | 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing | ||||||||||||||||
Veranstaltungsort: | Costa Rica | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 10 Dezember 2023 | ||||||||||||||||
Veranstaltungsende: | 13 Dezember 2023 | ||||||||||||||||
HGF - Forschungsbereich: | keine Zuordnung | ||||||||||||||||
HGF - Programm: | keine Zuordnung | ||||||||||||||||
HGF - Programmthema: | keine Zuordnung | ||||||||||||||||
DLR - Schwerpunkt: | Digitalisierung | ||||||||||||||||
DLR - Forschungsgebiet: | D IAS - Innovative autonome Systeme | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | D - STARE | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Kommunikation und Navigation > Nachrichtensysteme | ||||||||||||||||
Hinterlegt von: | Shutin, Dmitriy | ||||||||||||||||
Hinterlegt am: | 29 Nov 2023 19:16 | ||||||||||||||||
Letzte Änderung: | 24 Apr 2024 21:00 |
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