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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21210
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dc.contributor.authorFiguet, Benoit-
dc.contributor.authorMonstein, Raphael-
dc.contributor.authorFelux, Michael-
dc.date.accessioned2021-01-07T14:30:30Z-
dc.date.available2021-01-07T14:30:30Z-
dc.date.issued2020-12-01-
dc.identifier.issn2504-3900de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21210-
dc.description.abstractIn this paper, we present an aircraft localization solution developed in the context of the Aircraft Localization Competition and applied to the OpenSky Network real-world ADS-B data. The developed solution is based on a combination of machine learning and multilateration using data provided by time synchronized ground receivers. A gradient boosting regression technique is used to obtain an estimate of the geometric altitude of the aircraft, as well as a first guess of the 2D aircraft position. Then, a triplet-wise and an all-in-view multilateration technique are implemented to obtain an accurate estimate of the aircraft latitude and longitude. A sensitivity analysis of the accuracy as a function of the number of receivers is conducted and used to optimize the proposed solution. The obtained predictions have an accuracy below 25 m for the 2D root mean squared error and below 35 m for the geometric altitude.de_CH
dc.language.isoende_CH
dc.publisherMDPIde_CH
dc.relation.ispartofProceedingsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectOpenSky networkde_CH
dc.subjectADS-Bde_CH
dc.subjectLocalizationde_CH
dc.subjectMultilaterationde_CH
dc.subjectMachine learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc380: Verkehrde_CH
dc.titleCombined multilateration with machine learning for enhanced aircraft localizationde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitZentrum für Aviatik (ZAV)de_CH
dc.identifier.doi10.3390/proceedings2020059002de_CH
dc.identifier.doi10.21256/zhaw-21210-
zhaw.conference.details8th OpenSky Symposium, online, 12-13 November 2020de_CH
zhaw.funding.euNode_CH
zhaw.issue2de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume59de_CH
zhaw.publication.reviewOpen peer reviewde_CH
zhaw.webfeedSensorikde_CH
zhaw.webfeedSimulation and Optimizationde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Figuet, B., Monstein, R., & Felux, M. (2020). Combined multilateration with machine learning for enhanced aircraft localization [Conference paper]. Proceedings, 59(2). https://doi.org/10.3390/proceedings2020059002
Figuet, B., Monstein, R. and Felux, M. (2020) ‘Combined multilateration with machine learning for enhanced aircraft localization’, in Proceedings. MDPI. Available at: https://doi.org/10.3390/proceedings2020059002.
B. Figuet, R. Monstein, and M. Felux, “Combined multilateration with machine learning for enhanced aircraft localization,” in Proceedings, Dec. 2020, vol. 59, no. 2. doi: 10.3390/proceedings2020059002.
FIGUET, Benoit, Raphael MONSTEIN und Michael FELUX, 2020. Combined multilateration with machine learning for enhanced aircraft localization. In: Proceedings. Conference paper. MDPI. 1 Dezember 2020
Figuet, Benoit, Raphael Monstein, and Michael Felux. 2020. “Combined Multilateration with Machine Learning for Enhanced Aircraft Localization.” Conference paper. In Proceedings. Vol. 59. MDPI. https://doi.org/10.3390/proceedings2020059002.
Figuet, Benoit, et al. “Combined Multilateration with Machine Learning for Enhanced Aircraft Localization.” Proceedings, vol. 59, no. 2, MDPI, 2020, https://doi.org/10.3390/proceedings2020059002.


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