NLOS Identification and Mitigation for Localization
Author(s)Marano, Stefano; Gifford, Wesley Michael; Wymeersch, Henk; Win, Moe Z.
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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.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
IEEE Journal on Selected Areas in Communications
Institute of Electrical and Electronics Engineers
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.
Final published version
INSPEC Accession Number: 11523375