NLOS Identification and Mitigation for Localization
Name
Marano-2010-NLOS Identification.pdf
Size
1.56 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Marano, Stefano
Gifford, Wesley Michael
Wymeersch, Henk
Win, Moe Z.
Date Issued
September 2010
Journal
IEEE Journal on Selected Areas in Communications
Publisher
Institute of Electrical and Electronics Engineers
Citation
Marano, Stefano et al. “NLOS identification and mitigation for localization based on UWB experimental data.” IEEE Journal on Selected Areas in Communications 28 (2010): 1026-1035. ©2010 IEEE.
Version
Final published version
Abstract
Sensor networks can benefit greatly from location-awareness, since it allows information gathered by the sensors to be tied to their physical locations. Ultra-wide bandwidth (UWB) transmission is a promising technology for location-aware sensor networks, due to its power efficiency, fine delay resolution, and robust operation in harsh environments. However, the presence of walls and other obstacles presents a significant challenge in terms of localization, as they can result in positively biased distance estimates. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. From these channel pulse responses, we extract features that are representative of the propagation conditions. We then develop classification and regression algorithms based on machine learning techniques, which are capable of: (i) assessing whether a signal was transmitted in LOS or NLOS conditions; and (ii) reducing ranging error caused by NLOS conditions. We evaluate the resulting performance through Monte Carlo simulations and compare with existing techniques. In contrast to common probabilistic approaches that require statistical models of the features, the proposed optimization-based approach is more robust against modeling errors.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/jsac.2010.100907
