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Kernel principal component analysis for UWB-based ranging
Dept. of Electrical Engineering (ISY), Linkoping University, Sweden.
Dept. of Electrical Engineering (ISY), Linkoping University, Sweden.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences.
Swedish Defense Research Agency (FOI), Sweden.
2014 (English)In: 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2014, no October, 145-149 p.Conference paper, Published paper (Refereed)
Abstract [en]

Accurate positioning in harsh environments can enable many application, such as search-and-rescue in emergency situations. For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning. However, it still faces a problem in non-line-of-sight (NLOS) environments, in which range estimates based on time-of-arrival (TOA) are positively biased. There are many techniques that try to address this problem, mainly based on NLOS identification and NLOS error mitigation. However, these techniques do not exploit all available information from the UWB channel impulse response. In this paper, we propose a novel ranging technique based on kernel principal component analysis (kPCA), in which the selected channel parameters are projected onto nonlinear orthogonal high-dimensional space, and a subset of these projections is then used for ranging. We tested this technique using UWB measurements obtained in a basement tunnel of Linköping university, and found that it provides much better ranging performance comparing with standard techniques based on PCA and TOA. 

Place, publisher, year, edition, pages
2014. no October, 145-149 p.
Keyword [en]
kernel principal component analysis, machine learning, ranging, time-of-arrival, ultra-wideband, Artificial intelligence, Broadband networks, Impulse response, Learning systems, Mobile telecommunication systems, Range finding, Signal processing, Time of arrival, Ultra-wideband (UWB), Wireless telecommunication systems, Emergency situation, High dimensional spaces, Kernel principal component analyses (KPCA), NLOS error mitigations, Nlos identifications, Non-line-of-sight environments, Time of arrival (TOA), Ultra-wideband technology, Principal component analysis
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hig:diva-20074DOI: 10.1109/SPAWC.2014.6941337ISI: 000348859000030Scopus ID: 2-s2.0-84932613736OAI: oai:DiVA.org:hig-20074DiVA: diva2:844887
Conference
15th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2014, 22-25 June 2014, Toronto
Available from: 2015-08-10 Created: 2015-08-10 Last updated: 2017-04-03Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard-cite-them-right
  • ieee
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  • Other style
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Language
  • sv-SE
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  • de-DE
  • Other locale
More languages
Output format
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