Within the next decades the railway systems will change to fully autonomous high speed trains (HSTs). An increase in efficiency and safety and a reduction of costs would go hand in hand. Today's centralized railway management system and established regulations can not cope with trains driving within the absolute braking distance as it would be necessary for electronic coupling or platooning maneuvers. Hence, to ensure safety and reliability, new applications and changes in the train control and management are necessary. Such changes demand new reliable control communication links between train-to-train (T2T) and future developments on train-to-ground (T2G). T2G will be covered by long term evolution-railway (LTE-R) which shall replace today's global system for mobile communications-railway (GSM-R). The decentralized T2T communication is hardly investigated and no technology has been selected. This publication focuses on the wide band propagation for T2T scenarios and describes a extensive channel sounding measurement campaign with two HSTs. First results of T2T communication at high speed conditions in different environments are presented. Index Terms-train-to-train, high speed train, propagation, measurement.
In this paper a train localization method is proposed that uses local variations of the earth magnetic field to determine the topological position of a train in a track network. The approach requires a magnetometer triad, an accelerometer, and a map of the magnetic field along the railway tracks. The estimated topological position comprises the along-track position that defines the position of the train within a certain track and the track ID that specifies the track the train is driving on. The along-track position is estimated by a recursive Bayesian filter and the track ID is found from a hypothesis test. In particular the use of multiple particle filter, each estimating the position on different track hypothesis, is proposed. Whenever the estimated train position crosses a switch, a particle filter for each possible track is created. With the position estimates of the different filters, the likelihood for each track hypothesis is calculated from the measured magnetic field and the expected magnetic field in the map. A comparison of the likelihoods is subsequently used to decide which track is the most likely. After a decision for a track is made, the unnecessary filters are deleted. The feasibility of the proposed localization method is evaluated with measurement data recorded on a regional train. In the evaluation, the localization method was running in real time and overall an RMSE below five meter could be achieved and all tracks were correctly identified.
Magnetic field localization utilizes position dependent and time persistent distortions of the earth magnetic field. These distortions are introduced by stationary ferromagnetic material in the environment and can be stored in a map to enable localization. Estimating the position of a magnetometer with these distortions requires a calibration of the sensor to enable the matching of the measurements to the map. Typically, the calibration is performed in a prior step and requires specific maneuvers like sensor rotations in a homogenous field. The goal of the maneuvers is to render the calibration parameters observable. For heavy platforms, e.g., cars, trains and driverless transport systems in factories, performing special maneuvers is cumbersome or even impossible. In addition they operate in an environment with an inhomogeneous magnetic field. To address this issue, this paper proposes a novel method that exploits the magnetic field distortions to render the calibration parameters observable. To simplify the calibration process, the calibration parameters are estimated simultaneously with the position of the platform. The method employs a Rao-Blackwellized particle filter that reduces the computational complexity and enables real time processing. The feasibility of the method is shown in an evaluation with measurements of a magnetometer mounted on a model train. The results show a high accuracy of the position and calibration parameter estimation.
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