Train-borne localization systems as a key component of future signalling systems are expected to offer huge economic and operational advances for the railway transportation sector. However, the reliable provision of a trackselective and constantly available location information is still unsolved and prevents the introduction of such systems so far. A contribution to overcome this issue is presented here. We show a recursive multistage filtering approach with an increased cross-track positioning accuracy, which is decisive to ensure track-selectivity. This is achieved by exploiting track-geometry constraints known in advance, as there are strict rules for the construction of railway tracks. Additionally, compact geometric track-maps can be extracted during the filtering process which are beneficial for existing train localization approaches. The filter was derived applying approximate Bayesian inference. The geometry constraints are directly incorporated in the filter design, utilizing an interacting multiple model (IMM) filter and extended Kalman filters (EKF). Throughout simulations the performance of the filter is analyzed and discussed thereafter.

Critical infrastructures are the backbone of our societies with increasingly complex and networked characteristics and high availability demands. This makes them vulnerable to a wide range of threats that can lead to major incidents. Resilience is a concept that describes a system’s ability to absorb and respond to disturbances, as well as to learn from the past and anticipate new threats. In this article, we apply the Digital Twin concept to the infrastructure domain to improve the system’s resilience capabilities. We conduct a comprehensive requirements analysis related to infrastructure characteristics, crisis management and resilience measures. As a result, we propose a Digital Twin Conceptual Framework for critical infrastructures. We conclude that the Digital Twin paradigm is well suited to enhance critical infrastructure resilience.

In this paper, we present a method to generate compact geometric track-maps for train-borne localization applications. Therefore, we first give a brief overview on the purpose of track maps in train-positioning applications. It becomes apparent that there are hardly any adequate methods to generate suitable geometric track-maps. This is why we present a novel map generation procedure. It uses an optimization formulation to find the continuous sequence of track geometries that fits the available measurement data best. The optimization is initialized with the results from a localization filter [1] developed in our previous work. The localization filter also provides the required information for shape identification and measurement association. The presented approach will be evaluated on simulated data as well as on real measurements.

This paper describes an openly available data set for rail vehicle positioning experiments. The data were collected using the DLR research vehicle RailDriVE on a segment of the harbor railway of Braunschweig, Germany, in February 2019. Several sensors of the RailDriVE equipment and an additional self-sufficient system provided by Technische Universität Darmstadt were employed, including two GNSS receivers, two inertial measurement units (IMU), and several speed and distance sensors (radar, optical, odometer) from the rail domain. Front-facing camera data has been included for documentation purposes. In order to simplify its use, some pre-processing steps were applied to the data, mainly to have common time and coordinate frames. Furthermore, example and reference positioning solutions have been included. The data set is described in detail, with information about the individual sensors and the data set structure (with parameters, raw, pre-processed, and reference data). Our work should be seen as a step towards more open and data-driven research in the rail domain, where experiments are difficult and costly. It is our hope to provide a solid basis for many different research efforts that provide the required technological advances for the rail sector.