Utilizing Ensemble Learning to Improve the Distance Information for UWB Positioning

An ultra-wideband (UWB) positioning system consists of at least three anchors and a tag for the positioning procedure. Via the UWB transceivers mounted on all devices in the system, we can obtain the distance information between each pair of devices and further realize the tag localization. However,...
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Autor*in:	Che-Cheng Chang [verfasserIn] Yee-Ming Ooi [verfasserIn] Shih-Tung Tsui [verfasserIn] Ting-Hui Chiang [verfasserIn] Ming-Han Tsai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	positioning system distance information ensemble learning machine learning

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 12(2022), 19, p 9614
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:19, p 9614

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app12199614

Katalog-ID:	DOAJ086424629

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callnumber-first	T - Technology
author	Che-Cheng Chang
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title	Utilizing Ensemble Learning to Improve the Distance Information for UWB Positioning
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title_auth	Utilizing Ensemble Learning to Improve the Distance Information for UWB Positioning
abstract	An ultra-wideband (UWB) positioning system consists of at least three anchors and a tag for the positioning procedure. Via the UWB transceivers mounted on all devices in the system, we can obtain the distance information between each pair of devices and further realize the tag localization. However, the uncertain measurement in the real world may introduce incorrect measurement information, e.g., time, distance, positioning, and so on. Therefore, we intend to incorporate the technique of ensemble learning with UWB positioning to improve its performance. In this paper, we present two methods. The experimental results show that our ideas can be applied to different scenarios and work well. Of note, compared with the existing research in the literature, our first algorithm was more accurate and stable. Further, our second algorithm possessed even better performance than the first. Moreover, we also provide a comprehensive discussion for an ill-advised point, which is often used to evaluate the positioning efficiency in the literature.
abstractGer	An ultra-wideband (UWB) positioning system consists of at least three anchors and a tag for the positioning procedure. Via the UWB transceivers mounted on all devices in the system, we can obtain the distance information between each pair of devices and further realize the tag localization. However, the uncertain measurement in the real world may introduce incorrect measurement information, e.g., time, distance, positioning, and so on. Therefore, we intend to incorporate the technique of ensemble learning with UWB positioning to improve its performance. In this paper, we present two methods. The experimental results show that our ideas can be applied to different scenarios and work well. Of note, compared with the existing research in the literature, our first algorithm was more accurate and stable. Further, our second algorithm possessed even better performance than the first. Moreover, we also provide a comprehensive discussion for an ill-advised point, which is often used to evaluate the positioning efficiency in the literature.
abstract_unstemmed	An ultra-wideband (UWB) positioning system consists of at least three anchors and a tag for the positioning procedure. Via the UWB transceivers mounted on all devices in the system, we can obtain the distance information between each pair of devices and further realize the tag localization. However, the uncertain measurement in the real world may introduce incorrect measurement information, e.g., time, distance, positioning, and so on. Therefore, we intend to incorporate the technique of ensemble learning with UWB positioning to improve its performance. In this paper, we present two methods. The experimental results show that our ideas can be applied to different scenarios and work well. Of note, compared with the existing research in the literature, our first algorithm was more accurate and stable. Further, our second algorithm possessed even better performance than the first. Moreover, we also provide a comprehensive discussion for an ill-advised point, which is often used to evaluate the positioning efficiency in the literature.
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title_short	Utilizing Ensemble Learning to Improve the Distance Information for UWB Positioning
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