In this paper, we propose a distributed solution to the navigation of a population of unmanned aerial vehicles (UAVs) to best localize a static source. The network is considered heterogeneous with UAVs equipped with received signal strength (RSS) sensors from which it is possible to estimate the distance from the source and/or the direction of arrival through adhoc rotations. This diversity in gathering and processing RSS measurements mitigates the loss of localization accuracy due to the adoption of low-complexity sensors. The UAVs plan their trajectories on-the-fly and in a distributed fashion. The collected data are disseminated through the network via multi-hops, therefore being subject to latency. Since not all the paths are equal in terms of information gathering rewards, the motion planning is formulated as a minimization of the uncertainty of the source position under UAV kinematic and anti-collision constraints and performed by 3D non-linear programming. The proposed analysis takes into account non-line-of-sight (NLOS) channel conditions as well as measurement age caused by the latency constraints in communication.
Anna Guerra, Davide Dardari, Petar M. Djuric (2019). Non-Centralized Navigation for Source Localization by Cooperative UAVs. IEEE Institute of Electrical and Electronics Engineers [10.23919/EUSIPCO.2019.8902944].
Non-Centralized Navigation for Source Localization by Cooperative UAVs
Anna Guerra
;Davide Dardari;
2019
Abstract
In this paper, we propose a distributed solution to the navigation of a population of unmanned aerial vehicles (UAVs) to best localize a static source. The network is considered heterogeneous with UAVs equipped with received signal strength (RSS) sensors from which it is possible to estimate the distance from the source and/or the direction of arrival through adhoc rotations. This diversity in gathering and processing RSS measurements mitigates the loss of localization accuracy due to the adoption of low-complexity sensors. The UAVs plan their trajectories on-the-fly and in a distributed fashion. The collected data are disseminated through the network via multi-hops, therefore being subject to latency. Since not all the paths are equal in terms of information gathering rewards, the motion planning is formulated as a minimization of the uncertainty of the source position under UAV kinematic and anti-collision constraints and performed by 3D non-linear programming. The proposed analysis takes into account non-line-of-sight (NLOS) channel conditions as well as measurement age caused by the latency constraints in communication.| File | Dimensione | Formato | |
|---|---|---|---|
|
EUSIPCO_IEEE.pdf
accesso riservato
Tipo:
Versione (PDF) editoriale / Version Of Record
Licenza:
Licenza per accesso riservato
Dimensione
616.42 kB
Formato
Adobe PDF
|
616.42 kB | Adobe PDF | Visualizza/Apri Contatta l'autore |
|
EUSIPCO_final-postprint_concopertina.pdf
Open Access dal 19/05/2020
Descrizione: Post-print con disclaimer
Tipo:
Postprint / Author's Accepted Manuscript (AAM) - versione accettata per la pubblicazione dopo la peer-review
Licenza:
Licenza per accesso libero gratuito
Dimensione
704.37 kB
Formato
Adobe PDF
|
704.37 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


