A Particle Filter Based Train Localization Scheme Using Wireless Sensor Networks

Real-time train localization is essential to ensure the safety of modern railway transportation. This paper investigates the feasibility to achieve real-time and accurate train localization using wireless sensor networks. We carry out on-site experiments in a railway environment and demonstrate that Received Signal Strength Indicator (RSSI) is a good estimator for train localization. By combining the advantages of RSSI-based distance estimation and particle filtering techniques, we design a particle filter based train localization scheme and propose a novel Weighted RSSI Likelihood Function (WRLF) for updating the weights of particles. The proposed scheme is evaluated through simulations using the data obtained from the on-site measurements. Simulation results demonstrate that our scheme can achieve high localization accuracy, and is robust to changes in train speed and the deployment density of anchor sensors.