Benjamin Siebler

Train-side passive magnetic measurements

Passive magnetic sensors can measure the magnetic field density in three orthogonal axes and are often integrated on a single chip. These sensors are low-cost sensors and widely used in car navigation as well as in battery powered navigation equipment such as smartphones. There, its general purpose is the measurement of the heading angle by an electronically gimbaled compass technique. We are interested in train localization with multiple, exclusively onboard sensors and a track map. This approach is considered as a base technology for future railway applications such as train-centric train control, collision avoidance systems and autonomous driving. It has been shown that active magnetic measurements, such as a metal detector, is very beneficial for onboard train localization.

Relative train localization for cooperative maneuvers using GNSS pseudoranges and geometric track information

Cooperative maneuvers between trains like platooning or dynamic merging and splitting of train sets have the potential to increase efficiency and flexibility in railway operations. However, such maneuvers are safety critical and require continuously accurate relative position and velocity information. This information cannot be provided by infrastructure based train control systems like the European Train Control System, which is limited both in accuracy and update rate. Since the braking distance of trains often exceeds the visible track length, line-of-sight sensors alone are not sufficient for merging and splitting in all situations. We therefore present a Bayesian estimation algorithm that jointly estimates absolute and relative train positions and velocities based on GNSS pseudorange measurements and track map information for increased positioning performance. By transmitting GNSS pseudorange measurements between vehicles, common measurement errors can be eliminated, yielding better relative positioning accuracy. In simulations we show that the proposed algorithm achieves reduced absolute and relative positioning errors compared to algorithms using preprocessed GNSS positions and velocities as measurement input.

Study of Train-Side Passive Magnetic Measurements with Applications to Train Localization

Passive magnetic sensors measure the magnetic field density in three axes and are often integrated on a single chip. These low-cost sensors are widely used in car navigation as well as in battery powered navigation equipment such as smartphones as part of an electronic compass. We focus on a train localization application with multiple, exclusively onboard sensors and a track map. This approach is considered as a base technology for future railway applications such as collision avoidance systems or autonomous train driving. In this paper, we address the following question: how beneficial are passive magnetic measurements for train localization? We present and analyze measurements of two different magnetometers recorded on a regional train at regular passenger service. We show promising correlations of the measurements with the track positions and the traveled switch way. The processed data reveals that the railway environment has repeatable, location-dependent magnetic signatures. This is considered as a novel approach to train localization, as the use of these magnetic signals at first view is not obvious. The proposed methods based on passive magnetic measurements show a high potential to be integrated in new and existing train localization approaches.

Posterior Cramér-Rao bound and suboptimal filtering for IMU/GNSS based cooperative train localization

The optimal estimator for the hidden state of nonlinear systems is often not known or it is computational unfeasible. In this situation suboptimal algorithms must be used. An important performance metric for these algorithms is the difference of their root mean square error (RMSE) compared to the RMSE of the optimal estimator. If the optimal estimator is unknown it is useful to have a lower bound for the RMSE. Such a bound is defined by the posterior Cramér-Rao lower bound (PCRB) which is also valid for biased estimators. In this paper a version of the PCRB for nonlinear systems considering known inputs is applied to analyze the performance of an extended Kalman filter (EKF) and unscented Kalman filter (UKF) for GNSS/IMU based cooperative train localization. The analysis is realized by performing a simulation study for different track and satellite geometries. After an initial phase with larger errors both, the EKF and UKF are able to estimate the bias on the pseudoranges and the receiver clocks and they attain the PCRB.