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Open AccessProceedings

Combined Multilateration with Machine Learning for Enhanced Aircraft Localization

Centre for Aviation, School of Engineering, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland
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Presented at the 8th OpenSky Symposium 2020, Online, 12–13 November 2020.
Proceedings 2020, 59(1), 2; https://doi.org/10.3390/proceedings2020059002
Published: 1 December 2020
(This article belongs to the Proceedings of 8th OpenSky Symposium 2020)
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.
Keywords: OpenSky Network; ADS-B; localization; multilateration; machine learning OpenSky Network; ADS-B; localization; multilateration; machine learning
MDPI and ACS Style

Figuet, B.; Monstein, R.; Felux, M. Combined Multilateration with Machine Learning for Enhanced Aircraft Localization. Proceedings 2020, 59, 2. https://doi.org/10.3390/proceedings2020059002

AMA Style

Figuet B, Monstein R, Felux M. Combined Multilateration with Machine Learning for Enhanced Aircraft Localization. Proceedings. 2020; 59(1):2. https://doi.org/10.3390/proceedings2020059002

Chicago/Turabian Style

Figuet, Benoit; Monstein, Raphael; Felux, Michael. 2020. "Combined Multilateration with Machine Learning for Enhanced Aircraft Localization" Proceedings 59, no. 1: 2. https://doi.org/10.3390/proceedings2020059002

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