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Abstract
In recent years, research on the detection and mitigation of non-line-of-sight (NLOS) conditions in the context of ultra-wideband ranging has received increasing attention. As a result, numerous statistical and machine learning methods have been proposed, and a selection of datasets has been made available to the community. In an attempt to benchmark the performance of state-of-the-art NLOS classification and error correction techniques on a newly-built ultra-wideband testbed at our premises, we have observed how reusing publicly-available datasets and applying existing solutions is a complex and error-prone task. Indeed, a multitude of minor details in the selection, pre-processing, collection, labeling, and blending of datasets can have a profound impact on the correctness of the employed methods and on the achieved performance. In this paper, we summarize the lessons we have learned, pointing out potential pitfalls and distilling a few recommendations for researchers and practitioners approaching this research domain.
Originalsprache | englisch |
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Titel | Proceedings of 2023 Cyber-Physical Systems and Internet-of-Things Week, CPS-IoT Week 2023 - Workshops |
Herausgeber (Verlag) | Association of Computing Machinery |
Seiten | 78-83 |
Seitenumfang | 6 |
ISBN (elektronisch) | 9798400700491 |
DOIs | |
Publikationsstatus | Veröffentlicht - 9 Mai 2023 |
Veranstaltung | 2023 Cyber-Physical Systems and Internet-of-Things Week: CPS-IoT Week 2023 - San Antonio, USA / Vereinigte Staaten Dauer: 9 Mai 2023 → 12 Mai 2023 |
Publikationsreihe
Name | ACM International Conference Proceeding Series |
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Konferenz
Konferenz | 2023 Cyber-Physical Systems and Internet-of-Things Week |
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Kurztitel | CPS-IoT Week 2023 |
Land/Gebiet | USA / Vereinigte Staaten |
Ort | San Antonio |
Zeitraum | 9/05/23 → 12/05/23 |
ASJC Scopus subject areas
- Human-computer interaction
- Computernetzwerke und -kommunikation
- Maschinelles Sehen und Mustererkennung
- Software
Fields of Expertise
- Information, Communication & Computing
Fingerprint
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