Nonparametric obstruction detection for UWB localization

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.National Science Foundation (U.S.) (ECCS-0636519)National Institutes of Health (U.S.) (ECCS-0901034)United States. Office of Naval Research (Presidential Early Career Award for Scientists and Engineers (PECASE) N00014- 09-1-0435)Defense University Research Instrumentation Program (U.S.) (Grant N00014-08-1-0826)MIT/Army Institute for Soldier Nanotechnologie