A Machine Learning Approach to Ranging Error Mitigation for UWB Localization
Artikel i vetenskaplig tidskrift, 2012

Location-awareness is becoming increasingly important in wireless networks. Indoor localization can be enabled through wideband or ultra-wide bandwidth (UWB) transmission, due to its fine delay resolution and obstacle-penetration capabilities. A major hurdle is the presence of obstacles that block the line-of-sight (LOS) path between devices, affecting ranging performance and, in turn, localization accuracy. Many techniques have been proposed to address this issue, most of which make modifications to the localization algorithm. Since many localization algorithms work with distance or angle estimates, rather than received waveforms, information inherent in the wideband waveform is lost, leading to sub-optimal ranging error mitigation. To avoid this information loss, we present a novel approach to mitigate ranging errors directly in the physical layer. In contrast to existing techniques, which detect the non-line-of-sight (NLOS) condition, our approach directly mitigates the bias incurred in both LOS and non-LOS conditions. In particular, we apply two classes of non-parametric regressors to form an estimate of the ranging error. Our work is based on, and validated by, an extensive indoor measurement campaign with FCC-compliant UWB radios. The results show that the proposed regressors provide significant performance improvements in various practical localization scenarios, compared to conventional approaches.

Bayesian learning

support vector machine

Gaussian processes

ranging error mitigation




Henk Wymeersch

Chalmers, Signaler och system, Kommunikation, Antenner och Optiska Nätverk

Stefano Maranò

Eidgenössische Technische Hochschule Zürich (ETH)

Wesley Gifford

IBM Thomas J. Watson Research Center

Moe Win

Massachusetts Institute of Technology (MIT)

IEEE Transactions on Communications

0090-6778 (ISSN) 15580857 (eISSN)

Vol. 60 6 1719 - 1728 6192275


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