This paper presents measurements of train motion with a low-cost inertial measurement unit (IMU) based on micro electro mechanical systems (MEMS). The measurements were recorded on-board a train during normal passenger transport service on a network with dense urban railway environment as well as a rural, regional network environment. Sensor measurements from several train runs were therefore analyzed and the data is presented with a discussion on typical characteristics, noise and dynamics. As the train motion is dependent on the track, local track characteristics are inferred from the train motion measurements. Finally, the inertial measurements are analyzed toward track feature detection for feature based localization purposes.

The Ground Based Augmentation System (GBAS) is the cornerstone for enabling automated landings without the Instrument Landing System (ILS). Currently GBAS is evolving to GAST-D for CAT III landings. This extends GBAS via the use of multiple frequencies (L1/L2 and L5) and the use of multiple global navigation satellite system constellations. GBAS requires correction data to be broadcast to aircraft. This is currently done with the VHF Data Broadcast (VDB) datalink. However, VDB has several known shortcomings: (1) low throughput, (2) small area of operation and (3) no cyber-security measures. In this paper we propose the use of the L-band Digital Aeronautical Communications System (LDACS) for broadcasting GBAS correction data to address these shortcomings. In flight experiments conducted in 2019, we set up an experimental GBAS installation using LDACS. Broadcast data was secured using the TESLA broadcast authentication protocol. Our results indicate that cryptographically secured GBAS data via LDACS can provide GAST-C and GAST-D services with high availability if cryptographic parameters are chosen appropriately.

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The L-band Digital Aeronautical Communications System (LDACS) is a key enabler of the new air traffic services and operational concepts necessary for the modernization of the air traffic management (ATM). After its initial design, compatibility tests with legacy L-band systems, and functional demonstrations in the laboratory, the system is currently undergoing the standardization process of the International Civil Aviation Organization (ICAO). However, LDACS has not been demonstrated in flight yet. In this paper, we present the first in-flight demonstration of LDACS, which took place in March and April 2019 in southern Germany and included four LDACS ground stations and one LDACS airborne station. We detail the experimental setup of the implemented LDACS ground and airborne stations together with the flight routes, the conducted experiments, and the frequency planning to ensure compatibility with legacy systems. In addition, we describe the demonstrated ATM applications and the security measures used to protect them. Based on the obtained measurement results, we evaluate the LDACS in-flight communication performance for the first time, including the achieved communication range, the measured end-to-end message latency, and the LDACS capability to provide quality of service by effectively prioritizing safety-relevant data traffic. Furthermore, we use the in-flight received signal power to assess the applicability of a theoretical path loss model. These flight trials contribute to the final steps in the development of LDACS by providing its in-flight communication performance and by demonstrating: first, its correct functionality in a realistic environment; second, its capability of supporting ATM applications and the advanced security measures that can be used to protect them; and third, its spectrum compatibility with legacy systems. We conclude that LDACS is ready to support ATM operations and that LDACS frequency planning can safeguard legacy systems successfully.

The localization of trains in a railway network is necessary for train control or applications such as autonomous train driving or collision avoidance systems. Train localization is safety critical and therefore the approach requires a robust, precise and track selective localization. Satellite navigation systems (GNSS) might be a candidate for this task, but measurement errors and the lack of availability in parts of the railway environment do not fulfill the demands for a safety critical system. Therefore, additional onboard sensors, such as an inertial measurement unit (IMU), odometer and railway feature classification sensors (e.g. camera) are proposed. In this paper we present a top-down train localization approach from theory. We analyze causal dependencies and derive a general Bayesian filter. Furthermore we present a generic algorithm based on particle filter in order to process the multi-sensor data, the train motion and a known track map. The particle filter estimates a topological position directly in the track map without using map matching techniques. First simulations with simplified particular state and measurement models show encouraging results in critical railway scenarios.