Engage catalyst fund project summaries and reporting

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).
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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 (‘ATM-cybersec’)

Thematic challenge: 1 – Vulnerabilities and global security of the CNS/ATM system
Project coordinator: University of Jyväskylä, Finland
Partners: N/A
Abstract:
During the last decade, cybersecurity started to increasingly become an issue because many ATM/ATC/aviation stakeholders rely on electronic systems for critical parts of their operations, including safety-critical functions in avionics and related software/firmware. This project aims at closing this gap by developing a proof-of-concept practical, flexible, affordable pentesting platform for ATM/avionics cybersecurity. For this purpose we have developed from scratch a novel and unique end-to-end early stage (TRL3-4) platform as well as a comprehensive hardware/software testbed. With these, we have performed several hundreds of experimental iterations and developed four novel attacks while implementing altogether more than ten attacks. After pentesting more than 120 cumulative testbed configurations, we have discovered more than 40 vulnerabilities (e.g., Denial-of-Service, crashes, hangs) and a handful of logical and implementation bugs, all these posing imminent, realistic and dangerous cyber-physical threats to safe aviation/ATM/ATC. We also successfully re-purposed our platform for defensive mechanisms, such as ‘RSS-Distance’ model for detecting fake/spoofed ADS-B messages. Our methodologies and results are thoroughly documented in three distinct research manuscripts that currently undergo academic peer-review.
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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
Abstract:
The Safe Drone Flight (‘SDF’) project was led by NATS in collaboration with The Open University (OU) and funded by the SESAR Engage Knowledge Transfer Network (KTN) catalyst. The project investigated the security of unmanned flight surveillance systems and, in particular, the drone telemetry data they transmit. Developing a safety assured and cyber secure surveillance system is an important step in enabling U-space services, supporting safe, efficient and secure access to airspace for large numbers of drones. This project matured a prototype blockchain-based drone surveillance system taking a U-space scenario-based approach to simulate several drone operations and validate the concept’s suitability. Cyber security and safety assurance related research was conducted to determine data integrity-related design and performance requirements on the solution respectively.
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Download Use Cases and Scenarios deliverable

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
Abstract:
The project addresses a challenging approach for an environmentally friendly and more agile ATM framework by combining a Flight Centric ATC (FCA) approach and the Airstream concept. The day-to-day adaptation of the Airstream network to the demand of the airspace users will provide a resilient and scalable system for supporting Dynamic Airspace Configuration (DAC). Driven by the digitalisation of ATM, autonomous management of aircraft inside the Airstream is promoted. A computational framework is implemented for the evaluation of the concept. New aggregation methodologies are proposed for extracting main traffic flows (aggregated flights) from the initial demand. A simple mechanism for building the tri-dimensional structure of the Airstream network and flight allocation is then applied using the aggregation results. New trajectories of the Airstream network traffic are ultimately produced. Finally, comparison of the various traffic samples (i.e. original versus airstream) is performed through complexity evaluation. The metrics used, based on geometric information approach, have been improved for large spherical areas.
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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
Abstract:
The Meteo Sensors in the Sky (METSIS) project explores the use of drones as a wind sensor network for U-space applications. The novel concept aims to provide accurate and low-cost wind nowcasts for drones using data collected by drones themselves, i.e., ‘wind nowcasts for drones by drones’. A proof-of-concept flight-test experiment was performed using four drones to determine the feasibility of the METSIS concept at low altitudes. In the current incarnation, ultrasonic anemometers were mounted to each drone to measure local winds. The flight-tests evaluated the effect of obstacle-induced wind distortion, drone motion, measurement density, and measurement errors. Additionally, wind fields estimated during the flight-tests were published in real-time to the AirHub Drone Operations Center – a functional U-space Service Provider – to demonstrate the communication of these data to real end-users. The results indicate that the METSIS concept is a promising solution for wind nowcast component of the U-space weather information service. Future research should investigate the accuracy of the concept for a wider range of scenarios than considered here, and develop the technologies needed to increase the scalability of the concept.
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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
Abstract:
The objective of the PIU4TP project is the development of a data-driven methodology for the trajectory prediction from long to short term before scheduled time of flight. Specifically, the methodology uses machine learning and data mining techniques to perform data analysis and to learn from past experience the aircraft future behaviour in terms of flight path selection. Therefore, it exploits historical data and uncertainties of current forecasts of some relevant mission and aircraft parameters to compute trajectory prediction outcomes enriched with associated probabilistic information. The project’s final aim is to build a methodology that can support the Network Manager with air traffic flow and capacity management, allowing the optimisation of flight distribution among sectors and flight routes, the anticipation of air traffic flow requests and the identification in advance of potential conflicts.
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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
Abstract:
To support the safety of the ATM system, the future ATM architecture needs to deliver an exceptionally high level of cyber security. The objective of this project was therefore to advance cyber security management in several directions: (a) to develop a more collaborative approach to cyber security management; (b) to prototype these collaborative approaches; and (c) to adapt SESAR’s existing risk assessment methodology, ‘SecRAM’, to more quantitative methods, from which Bayesian Network analysis could be applied. The outputs of the project were a concept of operations for collaborative security management, a basic prototype for collaborative security management, and an approach for the application of Bayesian Networks. The prototype was developed to support a risk assessment that could be done in collaboration between several partners, such as by the members of a SESAR Solution Project. The outcome of the project is a step forward in information sharing, productivity and methods of knowledge exchange in cyber security.
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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
Abstract:
Virtualisation provides a path for air navigation service providers (ANSPs) to address the implementation of the open architecture proposed by the Airspace Architecture Study (AAS). Project RoMiAD has developed an understanding of the high-level benefits of deploying the distributed architecture proposed by the AAS and potential mechanisms to incentivise the organisational reengineering necessary to achieve a Digital European Sky whilst ensuring national sovereignty over airspace. During the course of Project RoMiAD, it has become clear that if virtualisation had been adopted before 2018 across Europe – ATM costs could have been 30% cheaper and there would have been no significant en-route delay – only unremovable delay would have remained e.g. caused by weather. 75% of the benefits come from improvements in the air traffic services (ATS) layer – increasing Air Traffic Controller Officer (ATCO) productivity and capacity sharing – and are best enabled by the flexibility that the common data layer provides. The focus to achieve the benefits needs to be on building alliances and collaborations within the ATS layer to ensure that the common data layer can support those collaborations.
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Final reporting available shortly

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
Abstract:
The WIPA – Weather Impact Prediction Tool for ATFCM initiative – has been launched by MetSafe and France Aviation Civile Services, in collaboration with Reims and Marseille Upper Area Control Centres. This one-year project addressed how the provision of weather hazards impact information on air traffic control sectors in intervals of one hour over the ATFCM horizon. To do so, WIPA considered the convection information as an input provided by the MET Enhanced ATFCM product (developed during the first catalyst wave), additional MET information (as real-time convection observation and SIGMET), and ATM information. The research approach focused on both technical and operational aspects, as needs identification, design of the tool and deployment via a SWIM webservice. Technical and operational validation trials showed that initial project objectives have been fulfilled: Reims and Marseille ATCOs and FMP operators highly improved their weather situational awareness and would likely have taken ATCFM measures based on received information.
Download the executive summary
Final reporting available shortly