Model-based and empirical performance analyses for passenger positioning algorithms in a specific bus cabin environment

Localization methods play an increasing role in many of today's Intelligent Transportation Systems (ITS). In the context of this work we concentrate on improving the localization accuracy of passengers in public transport vehicles, e.g. as part of automated registration, counting or ticketing systems. In these applications each passenger's ticket could be designed as a mobile wireless sensor node, which is registered to be inside or outside the vehicle using a network of fixed anchor nodes inside only. We shortly describe two specific positioning approaches (SMDS, GBP) out of a variety of methods. A set of measurements conducted in a realistic bus cabin environment is then used to analyze the performance of both algorithms. The accuracy of the proposed GBP-estimator is compared with a previously studied SMDS-estimator, particularly in severe multipath environments like in the given passenger cabin.