ROBUST FILTERING TECHNIQUES APPLIED TO TRAIN POSITIONING

State estimation methods allow the vehicle position and velocity to be reconstructed by combining information from sensors and vehicle modelling. In a railway application, measurement signals from several sensors are available at asynchronous times, e.g., signals from odometers, radars and accelerometers. A Kalman filter can be easily designed based on a linear discrete-time model. However, in the train security package, only one accelerometer is available and moreover, this accelerometer is sensitive to rail track gradient. To circumvent the problem of estimation bias, a robust filter is developed, which takes uncertainties on the acceleration measurements and asynchronous data into account.