Summaries and reports produced by projects receiving catalyst funding through Engage. Initial contact with projects can be made via Engage.
First wave projects (2019-2020)
C1. Probabilistic weather avoidance routes for medium-term storm avoidance (‘PSA-Met’)
Thematic challenge: 3 – Efficient provision and use of meteorological information in ATM
Project coordinator: University of Seville, Spain
Partners: MeteoSolutions GmbH, Germany
Abstract:
PSA-Met integrates new meteorological capabilities in the storm avoidance process, namely, probabilistic nowcasts. These new meteorological products provide not only a forecast of the storm’s evolution, but also information about the uncertainty of the convective cells. PSA-Met develops a probabilistic weather-avoidance concept, according to which, the required inputs are a probabilistic nowcast and a risk level, which is an adjustable parameter intended to define the avoidance strategy. The output is a unique avoidance trajectory that takes into account the uncertainty of the convective cells, obtained for the given risk level. Simulation results show that the predictability, the safety and the workload of pilots and air traffic controllers are improved, although with a small loss of flight efficiency. This new weather avoidance concept will be used in a follow-up project, whose objective will be to develop a Medium-Term Storm Avoidance tool intended to enhance air traffic control efficiency.
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C2. airport-sCAle seveRe weather nowcastinG project (‘CARGO’)
Thematic challenge: 3 – Efficient provision and use of meteorological information in ATM
Project coordinator: University of Padova, Italy
Partners: LMU Munich, Germany; GReD srl, Italy; Leonardo GmbH, Germany
Abstract:
This project has combined measurements from different instruments to develop a nowcasting algorithm of extreme weather events in a localised area around the Malpensa airport with the aim of improving aviation safety. Radar reflectivity has been used as reference to define and select the extremes; Global Navigation Satellite System (GNSS) zenith total delay, atmospheric parameters from weather stations, and lightning have been used as inputs of a neural network to predict the development of the weather events in the near future (from 30 to 90 minutes before). The results show an accuracy of 0.75 in nowcasting the extreme events when using all the datasets as inputs and decreasing accuracy when excluding one of the inputs. However, there are still several tests that should be performed to understand the optimal setting of the algorithm. This project was the first experiment to collect so many atmospheric sensors in a localised area to nowcast extreme events with ATM purposes and posed the basis to develop a deeper study on this field.
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C3. Authentication and integrity for ADS-B
Thematic challenge: 1 – Vulnerabilities and global security of the CNS/ATM system
Project coordinator: TU Kaiserslautern, Germany
Partners: SeRo Systems GmbH, Germany
Abstract:
The main objective of this project is to provide the means to improve the security of the Automatic Dependent Surveillance-Broadcast (ADS-B), a critical backbone of future surveillance systems. More specifically, we evaluate the data link capabilities of the so-called phase overlay, a backwards-compatible extension to the current implementation of ADS-B. Our results indicate that 8PSK performs best in a realistic radio environment, reliably providing up to 218 additional bits for each ADS-B message at a carrier frequency offset tolerance of about 40 kHz. Based on these insights, we propose a protocol that relies on the phase overlay to authenticate the information provided via the ADS-B.
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C4. Data-driven trajectory imitation with reinforcement learning
Thematic challenge: 2 – Data-driven trajectory prediction
Project coordinator: University of Piraeus Research Center, Greece
Partners: Boeing Research and Technology Europe, Spain
Abstract:
The objective of this project was to present algorithms for data-driven imitation of trajectories, following deep reinforcement learning techniques towards enhancing our trajectory prediction abilities. We aimed at building a data-driven approach in which the learning process is (a) an imitation process, where the algorithm tries to imitate ‘expert’, demonstrated trajectories, (b) exploiting raw trajectory data, enriched with contextual data (e.g. weather conditions etc) and (c) based on reward models (for producing trajectories in high-fidelity) that are learned during imitation. There are two main project contributions (i) a general framework for the prediction of trajectories in which deep imitation and reinforcement learning methods play a major role, together with methods selecting important features for decision making and future trajectory classification methods; and (ii) a developed and evaluated state of the art deep imitation learning techniques for predicting trajectories in the aviation domain, showing their potential for highly accurate prediction results, especially in long trajectories with multiple patterns / modalities, and in cases where the demonstrated trajectories are few.
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C5. A Data-drIven approach for dynamic and Adaptive trajectory PredictiON (‘DIAPasON’)
Thematic challenge: 2 – Data-driven trajectory prediction
Project coordinator: CRIDA, Spain
Partners: Deep Blue, Italy; ZenaByte, Italy
Abstract:
The DIAPasON project focuses on the need of the ATM system to develop tools and methodologies which are able to support traffic and trajectory management functions. For these activities, trajectory and traffic prediction is key, in particular within the context of Trajectory-Based Operations (TBO). While previous research exists addressing these matters, DIAPasON presents a different approach. In particular, the project aims at analysing patterns of flight plan evolution for individual flights, and extract patterns and feature which can be applied in a wide number of operational contexts where this information is available. The main result of the project is the development of a methodology for trajectory prediction and traffic forecasting in a pre-tactical phase (from a few days to a few hours before the operations, when a only limited number of flight plans are available). This can be adjusted to different time scales (planning horizons), considering the level of predictability of each of them and the specific use case to where it should be applied. These results have been explored with support of operational staff to maximise the benefits in the pre-tactical phase.
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C6. Operational alert Products for ATM via SWIM (‘OPAS’)
Thematic challenge: 3 – Efficient provision and use of meteorological information in ATM
Project coordinator: Royal Belgian Institute for Space Aeronomy, Belgium
Partners: N/A
Abstract:
Volcanic emission is a threat to ATM and the safety of flights. Early warnings are an essential source of information for stakeholders. The OPAS project is the development of a SWIM Technical Infrastructure Yellow Profile service providing information (notification & data access) about volcanic SO2 height. The OPAS service considers observations from three hyperspectral satellite sensors (TROPOMI, IASI-A and IASI-B), respectively operating in the ultraviolet and infrared ranges. These instruments represent the state of the art of satellite SO2 measurements. The IASI sensor already provides well recognised estimations of SO2 height, which is available through the SACS early warning system and contributes to the OPAS service. The outcome of the OPAS project is the new algorithmic development (iterative SO2 optical depth fitting) of TROPOMI SO2 height retrievals, the creation of alerts and access to tailored information, i.e. SO2 contamination of flight level and improved mass loading estimates.
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C7. An interaction metric for an efficient traffic demand management: requirements for the design of data-driven protection mechanisms (‘INTERFACING’)
Thematic challenge: 2 – Data-driven trajectory prediction
Project coordinator: Aslogic, Spain
Partners: N/A
Abstract:
A major limitation of the current ATM system is the loss of effectiveness due to the limited integration between the layered planning Decision Support Tools (DSTs). While the Trajectory Based Operation concept enables new DSTs that could deal with present demand/capacity, a word of caution at a practical level: ATM stakeholders realise that technological flexibility to regulate flights into a sector is not synonymous of performance, rather several negative effects can arise at the network level due to lack of analysis of interdependencies among regulated sectors. INTERFACING has developed a formal probabilistic framework to detect and characterise at the network level the flight interactions and their interdependencies. New interaction metrics have been implemented to enable the evaluation of regulation efficiency and to pave the way for the design of mitigation measures for a smooth fine-tuning of traffic demand at a micro level that considers the effects at a macro level improving the network performance.
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C8. MET enhanced ATFCM
Thematic challenge: 3 – Efficient provision and use of meteorological information in ATM
Project coordinator: FRACS (formerly DSNA Services), France
Partners: MetSafe, France
Abstract:
The MET Enhanced ATFCM R&D initiative has been launched by MetSafe and France Aviation Civile Services. This one-year project addressed the provision of accurate convection information for ATFCM activities, with the 6 hours’ time-horizon as a target. The research approach focused on both technical and operational aspects, as needs identification and concept of operations, assessment of convection models, design and deployment of a model-based R&D convection product. Up-to-date and accurate European thunderstorm forecasts at +6 hours horizon built from a multi weather model algorithm have been delivered as a SWIM webservice for Reims Upper Area Control Centre during technical and operational validation trials. Initial project objectives have been fulfilled: Reims air traffic controllers and FMP operators greatly improved their weather situational awareness and would have been likely to take ATCFM measures based on received information.
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C9. Exploring future UDPP concepts through computational behavioural economics
Thematic challenge: 4 – Novel and more effective allocation markets in ATM
Project coordinator: Nommon Solutions and Technologies, Spain
Partners: N/A
Abstract:
When the demand of an airspace sector is expected to exceed capacity, flights are delayed and assigned new take-off times through ATFM slots. This delay represents a significant cost for airlines and passengers. The possibility of rearranging flight sequences offers remarkable potential to reduce the impact of ATFM delay. Several prioritisation instruments are proposed in the literature, but their implementation is hindered by the limitations of classical modelling approaches to represent Airspace Users (AUs) behaviour and network effects in a realistic manner. The aim of the project is to overcome these limitations through the combined use of agent-based modelling (ABM) and behavioural economics. The model developed by the project has been used to simulate the performance of a variety of flight prioritisation under different network conditions and AU behaviours, allowing the observation of emergent phenomena and opening the way for a rigorous and comprehensive assessment of innovative approaches to User Driven Prioritisation Process (UDPP).
Download the executive summary
Download the final technical report
Download D1.1 UDPP Assessment Framework: Indicators and Metrics
Download D2.1 Tactical Slot and Trajectory Allocation Mechanisms: Qualitative Assessment
Download D3.1 Agent-Based Simulation Model for the Analysis of Tactical Slot and Trajectory Allocation Mechanisms
Download D4.1 Results of Simulation Experiments: Comparative Analysis of Different Tactical Slot and Trajectory Allocation Mechanisms
C10. The drone identity – investigating forensic-readiness of U-Space services
Thematic challenge: 1 – Vulnerabilities and global security of the CNS/ATM system
Project coordinator: Open University, UK
Partners: NATS, UK
Abstract:
The Drone Identity project investigates forensic-readiness requirements of unmanned aerial systems (UAS), to help identify causes of safety and security related air traffic incidents. It is a collaborative effort between researchers at The Open University (OU) and NATS. The project contributes to addressing the vulnerabilities and global security of communications, navigation, and surveillance systems in air traffic management (CNS/ATM). The collection and use of forensic data associated with drones and surrounding physical contexts is key to effective investigation. The research is conducted in the context of U-Space, focusing on the architecture and concept of operations for European unmanned traffic management (UTM), and the ability to preserve such vital information as evidence for forensic investigations. The goals of such forensic readiness are to ensure that the root causes of incidents can always be analysed, facilitated by evidence collected during operation (drone flight). The project focuses on drone data, examining ways in which key drone characteristics can be determined and recorded soundly, if and when incidents involving the drone(s) occur. In particular, the key attributes that characterise and identify the drones, their operators, and their anomalous behaviours will be investigated. A prototype demonstrator has been developed, including a technical architecture, to illustrate and evaluate the proposed forensic readiness requirements for U-Space services.
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Second wave projects (2020-2021)
C11. Proof-of-concept: practical, flexible, affordable pentesting platform for ATM/avionics cybersecurity
Thematic challenge: 1 – Vulnerabilities and global security of the CNS/ATM system
Project coordinator: University of Jyväskylä, Finland
Partners: N/A
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C12. Safe drone flight – assuring telemetry data integrity in U-Space scenarios (‘SDF’)
Thematic challenge: 1 – Vulnerabilities and global security of the CNS/ATM system
Project coordinator: NATS, UK
Partners: Open University, UK
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C13. Flight centric ATC with airstreams (‘FC2A’)
‘Open’ (fits well with Thematic challenge: 2 – Data-driven trajectory prediction)
Project coordinator: NEOMETSYS, France
Partners: ENAC, France
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C14. Meteo Sensors In the Sky (‘METSIS’)
Thematic challenge: 3 – Efficient provision and use of meteorological information in ATM
Project coordinator: NLR, The Netherlands
Partners: AirHub, The Netherlands
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C15. Probabilistic information Integration in Uncertain data processing for Trajectory Prediction (‘PIU4TP’)
Thematic challenge: 2 – Data-driven trajectory prediction
Project coordinator: CIRA, Italy
Partners: N/A
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C16. Collaborative cyber security management framework
Thematic challenge: 1 – Vulnerabilities and global security of the CNS/ATM system
Project coordinator: Winsland, UK
Partners: Movable-type, UK; MSDK, Bulgaria; BULATSA, Bulgaria
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C17. Role of Markets in AAS Deployment (‘RoMiAD’)
Thematic challenge: 4 – Novel and more effective allocation markets in ATM
Project coordinator: Think Research, UK
Partners: N/A
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C18. Weather impact prediction for ATFCM (‘WIPA’)
Thematic challenge: 3 – Efficient provision and use of meteorological information in ATM
Project coordinator: FRACS (formerly DSNA Services), France
Partners: MetSafe, France
Download the project summary