Che, F, Ahmed, A, Ahmed, QZ et al. (2 more authors) (2020) Machine Learning Based Approach for Indoor Localization Using Ultra-Wide Bandwidth (UWB) System for Industrial Internet of Things (IIoT). In: IEEE Proceedings of the 2020 UK/China Emerging Technologies (UCET) conference. 5th International Conference on the UK-China emerging technologies (UCET) 2020, 20-21 Aug 2020, University of Glasgow, Glasgow, United Kingdom. IEEE ISBN 978-1-7281-9489-9
Abstract
With the rapid development of wireless communication technology and the emergence of the Industrial Internet of Things (IIoT)s applications, high-precision Indoor Positioning Services (IPS) are urgently required. While the Global Positioning System (GPS) has been a key technology for outdoor localization, its limitation for indoor environments is well known. UltraWideBand (UWB) can help provide a very accurate position or localization for indoor harsh propagation environments. This paper focuses on improving the accuracy of the UWB indoor localization system including the Line-of-Sight (LoS) and NonLine-of-Sight (NLoS) conditions by developing a Machine Learning (ML) algorithm. In this paper, a Naive Bayes (NB) ML algorithm is developed for UWB IPS. The performance of the developed algorithm is evaluated by Receiving Operating Curves (ROC)s. The results indicate that by employing the NB based ML algorithm significantly improves the localization accuracy of the UWB system for both the LoS and NLoS environment
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © IEEE 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | UWB , IPS , localization , ML , Naive Bayes. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Aug 2020 13:00 |
Last Modified: | 10 Dec 2020 00:53 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/UCET51115.2020.9205352 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164202 |