NLOS identification and mitigation for UWB localization based on experimental data
Journal article, 2010

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.

Author

Stefano Maranò

Swiss Federal Institute of Technology in Zürich (ETH)

Wesley Gifford

Massachusetts Institute of Technology (MIT)

Henk Wymeersch

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Moe Win

Massachusetts Institute of Technology (MIT)

IEEE Journal on Selected Areas in Communications

0733-8716 (ISSN)

Vol. 28 7 1026-1035 5555901

Areas of Advance

Information and Communication Technology

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/JSAC.2010.100907

More information

Latest update

4/11/2018