Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21210
Publication type: Conference paper
Type of review: Open peer review
Title: Combined multilateration with machine learning for enhanced aircraft localization
Authors: Figuet, Benoit
Monstein, Raphael
Felux, Michael
et. al: No
DOI: 10.3390/proceedings2020059002
10.21256/zhaw-21210
Published in: Proceedings
Volume(Issue): 59
Issue: 2
Conference details: 8th OpenSky Symposium 2020, Online, 12–13 November 2020
Issue Date: 1-Dec-2020
Publisher / Ed. Institution: MDPI
ISSN: 2504-3900
Language: English
Subjects: OpenSky network; ADS-B; Localization; Multilateration; Machine learning
Subject (DDC): 006: Special computer methods
380: Transportation
Abstract: In 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/21210
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Centre for Aviation (ZAV)
Appears in collections:Publikationen School of Engineering

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