Pregled bibliografske jedinice broj: 1012493
Drone Localization using Ultrasonic TDOA and RSS Signal – Integration of the Inverse Method of a Particle Filter
Drone Localization using Ultrasonic TDOA and RSS Signal – Integration of the Inverse Method of a Particle Filter // FME Transactions, 48 (2019), 21-30 doi:10.5937/fmet2001021S (međunarodna recenzija, članak, znanstveni)
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Naslov
Drone Localization using Ultrasonic TDOA and RSS Signal – Integration of the Inverse Method of a Particle Filter
Autori
Šoštarić, Damir ; Mester, Gyula
Izvornik
FME Transactions (1451-2092) 48
(2019);
21-30
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
WSN, TDOA-RSS method, Indoor localization, Probabilistic model, Particle filter.
Sažetak
This paper will present an overview of indoor and outdoor drone localization methods. Outdoor scenarios almost always use a GPS with IMU. Indoor systems using short-range sensors that are sensitive to the external conditions of the environment. Mostly used methods are optical flow and stereovision, while an ultrasonic transceiver system optimizes and provides high precision and orientation of the drone. An ultrasonic preceptor is integrated into a listener/beacon and can be used with referenced beacons inside a WSN. The Crossbow Cricket hardware platform, which is based on TDOA and RSS principle is used for simulations and code development. The researched direction is the localization of referent nodes (beacons) concerning the listener which is mounted on a flying drone. For that purpose, a probabilistic approach is used, based on a Bayes filter, where the positions of the beacon can be observed like random variables. Considering that these distributions significantly vary from a Gauss distribution, it is appropriate to use a particle filter.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Strojarstvo, Interdisciplinarne tehničke znanosti
Citiraj ovu publikaciju:
Časopis indeksira:
- Web of Science Core Collection (WoSCC)
- Emerging Sources Citation Index (ESCI)
- Scopus
- EconLit