{"title":"Aircraft Localization Using ATC Data with Nanosecond Precision from Distributed Crowdsourced Receivers","authors":"S. Markochev","doi":"10.3390/engproc2021013012","DOIUrl":null,"url":null,"abstract":"In this paper, we present the first place solution for the Aircraft Localization Competition, which was held on the AIcrowd platform between 15 June 2020 and 31 January 2021 and was organized by the OpenSky Network and the Cyber-Defence Campus of armasuisse Science and Technology. The data for the competition was collected by the OpenSky Network from hundreds of crowdsourced low-cost receivers with nanosecond precision timestamps. Many receivers experienced clock drift and random walk and even provided fully broken timestamps. The solution combines well-known multilateration positioning with a variety of filtering methods and two tailored models for radio wave propagation and receiver clock drift to predict unknown aircraft locations. In this solution, we managed to synchronize 241 receivers, including 36 GPS-equipped, and achieved 81.9 m RMSE 2D distance prediction accuracy on 70% of samples on the private leaderboard.","PeriodicalId":11748,"journal":{"name":"Engineering Proceedings","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/engproc2021013012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, we present the first place solution for the Aircraft Localization Competition, which was held on the AIcrowd platform between 15 June 2020 and 31 January 2021 and was organized by the OpenSky Network and the Cyber-Defence Campus of armasuisse Science and Technology. The data for the competition was collected by the OpenSky Network from hundreds of crowdsourced low-cost receivers with nanosecond precision timestamps. Many receivers experienced clock drift and random walk and even provided fully broken timestamps. The solution combines well-known multilateration positioning with a variety of filtering methods and two tailored models for radio wave propagation and receiver clock drift to predict unknown aircraft locations. In this solution, we managed to synchronize 241 receivers, including 36 GPS-equipped, and achieved 81.9 m RMSE 2D distance prediction accuracy on 70% of samples on the private leaderboard.
在本文中,我们提出了飞机本地化比赛的第一名解决方案,该比赛于2020年6月15日至2021年1月31日在aiccrowd平台上举行,由开放天空网络和armasuisse科学技术网络防御校区组织。比赛的数据是由开放天空网络从数百个众包的低成本接收器中收集的,这些接收器具有纳秒精度的时间戳。许多接收机经历了时钟漂移和随机游走,甚至提供了完全破碎的时间戳。该解决方案结合了多种滤波方法和两种定制的无线电波传播和接收机时钟漂移模型,以预测未知的飞机位置。在这个解决方案中,我们设法同步了241个接收器,其中包括36个配备gps的接收器,并在私人排行榜上70%的样本上实现了81.9 m RMSE 2D距离预测精度。