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 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2020_Figuet-etal_Combined-multilateration-with-machine-learning_Proceedings.pdf | 396.29 kB | Adobe PDF |  View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.