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Kernel Methods for Accurate UWB-Based Ranging with Reduced Complexity
Department of Electrical Engineering (ISY), Linköping University, Linköping, Sweden.
Department of Electrical Engineering (ISY), Linköping University, Linköping, Sweden .
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences.
Swedish Defense Research Agency (FOI), Linköping, Sweden .
2016 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 15, no 3, 1783-1793 p.Article in journal (Refereed) Published
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Abstract [en]

Accurate and robust positioning in multipath environments can enable many applications, such as search-and-rescue and asset tracking. For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning. However, UWB still faces a problem with non-line-of-sight (NLOS) measurements, in which the range estimates based on time-of-arrival (TOA) will typically be positively biased. There are many techniques that address this problem, mainly based on NLOS identification and NLOS error mitigation algorithms. However, these techniques do not exploit all available information in the UWB channel impulse response. Kernel-based machine learning methods, such as Gaussian process regression (GPR), are able to make use of all information, but they may be too complex in their original form. In this paper, we propose novel ranging methods based on kernel principal component analysis (kPCA), in which the selected channel parameters are projected onto a nonlinear orthogonal high-dimensional space, and a subset of these projections is then used as an input for ranging. We evaluate the proposed methods using real UWB measurements obtained in a basement tunnel, and found that one of the proposed methods is able to outperform state-of-the-art, even if little training samples are available.

Place, publisher, year, edition, pages
2016. Vol. 15, no 3, 1783-1793 p.
Keyword [en]
Artificial intelligence, Broadband networks, Gaussian distribution, Gaussian noise (electronic), Impulse response, Learning systems, Mobile telecommunication systems, Principal component analysis, Range finding, Time of arrival, Gaussian process regression, High dimensional spaces, Kernel principal component analyses (KPCA), Machine learning methods, Multi-path environments, NLOS error mitigations, positioning, Ultra-wideband technology, Ultra-wideband (UWB)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hig:diva-21491DOI: 10.1109/TWC.2015.2496584ISI: 000372627500013ScopusID: 2-s2.0-84963553390OAI: oai:DiVA.org:hig-21491DiVA: diva2:927803
Available from: 2016-05-13 Created: 2016-05-13 Last updated: 2017-02-22Bibliographically approved

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