Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Time of arrival estimation for UWB localizers in realistic environments
EURASIP Journal on Applied Signal Processing
Robust estimator for non-line-of-sight error mitigation in indoor localization
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Advances in Signal Processing
An improved algorithm for UWB-bases positioning in a multi-path environment
IZS '06 Proceedings of the 2006 International Zurich Seminar on Communications
Nonparametric obstruction detection for UWB localization
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
On the accuracy of localization systems using wideband antenna arrays
IEEE Transactions on Communications
Wideband diversity in multipath channels with nonuniform power dispersion profiles
IEEE Transactions on Wireless Communications
Multipath Aided Rapid Acquisition: Optimal Search Strategies
IEEE Transactions on Information Theory
IEEE Journal on Selected Areas in Communications
Characterization of ultra-wide bandwidth wireless indoor channels: a communication-theoretic view
IEEE Journal on Selected Areas in Communications
Ranging in a dense multipath environment using an UWB radio link
IEEE Journal on Selected Areas in Communications
A Cooperative Localization Algorithm for UWB Indoor Sensor Networks
Wireless Personal Communications: An International Journal
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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.