Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
An improved algorithm for UWB-bases positioning in a multi-path environment
IZS '06 Proceedings of the 2006 International Zurich Seminar on Communications
Ranging in a dense multipath environment using an UWB radio link
IEEE Journal on Selected Areas in Communications
LOS/NLOS detection for UWB signals: a comparative study using experimental data
ISWPC'10 Proceedings of the 5th IEEE international conference on Wireless pervasive computing
NLOS identification and mitigation for localization based on UWB experimental data
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
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Ultra-wide bandwidth (UWB) transmission is a promising technology for indoor localization due to its fine delay resolution and obstacle-penetration capabilities. However, the presence of walls and other obstacles introduces a positive bias in distance estimates, severely degrading localization accuracy. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. Based on this campaign, we extract key features that allow us to distinguish between NLOS and LOS conditions. We then propose a nonparametric approach based on support vector machines for NLOS identification, and compare it with existing parametric (i.e., model-based) approaches. Finally, we evaluate the impact on localization through Monte Carlo simulation. Our results show that it is possible to improve positioning accuracy relying solely on the received UWB signal.