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.

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.

Maximizing the coherent processing interval (CPI) is crucial when performing passive radar detection on weak signal reflections. In practice however, the CPI is limited by the target movement. In this work, the extent of the range and Doppler migration effects occurring when using a long CPI to integrate the returns from an L-band digital aeronautical communication system (LDACS) based passive radar is studied. In particular, our simulations underline the extensive Doppler migration effect that arises even for non-accelerating targets. To this end, the Keystone transform and fractional Fourier transform techniques are combined with the standard passive radar processing to enable the compensation of both range and Doppler migration effects. This non-model based approach is, however, shown to have limitations, in particular for low signal-to-noise ratios and/or multi-target scenarios. To address these shortcomings, a novel model-based framework that allows to perform joint target detection and parameter estimation is developed. For this, a superresolution sparse Bayesian learning approach is employed. This technique uses a multi-target observation model which accurately accounts for the underlying range and Doppler migration effects and provides super-resolution estimation capabilities. This is particularly advantageous in the LDACS case since the narrow bandwidth generally limits the separation of closely spaced targets. The simulation experiments demonstrate the effectiveness of the algorithm and the advantages it provides when compared to the standard migration compensation approach.

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