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ItemIntent-Aware MPC for Aircraft Detect-and-Avoid with Response Delay : A Comparative Study with ACAS Xu(EUROCONTROL, 2025)In this paper, we propose an intent-aware Model Predictive Control (MPC) approach for the remain-well-clear (RWC) functionality of a multi-agent aircraft detect-and-avoid (DAA) system and compare its performance with the standardized Airborne Collision Avoidance System Xu (ACAS Xu). The aircraft system is modeled as a linear system for horizontal maneuvering, with advisories on the rate of turn as the control input. Both deterministic and stochastic time delays are considered to account for the lag between control guidance issuance and the response of the aircraft. The capability of the MPC scheme in producing an optimal control profile over the entire horizon is used to mitigate the impact of the delay. We compare the proposed MPC method with ACAS Xu using various evaluation metrics, including loss of DAA well-clear percentage, near mid-air collision percentage, horizontal miss distance, and additional flight distance across different encounter scenarios. It is shown that the MPC scheme achieves better evaluation metrics than ACAS Xu for both deterministic and stochastic scenarios.
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ItemHUCAN: Towards Certification-Aware Design For Advanced Automation Solutions(EUROCONTROL, 2025)This paper presents the outcome of research carried out in the SESAR Joint Undertaking's HUCAN project, which aims to develop a holistic, unified certification framework for highly automated systems, to ensure their safe and efficient integration into air traffic management. Based on an analysis of current and innovative certification approaches, the project developed two solutions: 1) a structured, iterative methodology that facilitates certification alignment and validation of advanced automation; 2) preliminary guidelines, which, based on a gap analysis between EASA AI guidance for Level 1 and 2 Machine Learning (ML) applications and the SESAR Project Handbook, offer directions for addressing these gaps along the research pipeline. The approach proposed by these solutions offers significant benefits by promoting an early and proactive alignment of design strategies with certification objectives, thereby mitigating late-stage risks and fostering a more efficient and predictable path to deployment for these innovative systems.
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ItemTesting of Two-Phase Cooling of Bipolar Plates for Fuel Cells(IEEE, 2025)Hydrogen-powered fuel cells are the preferred energy source for electric aircraft. However, for aircraft applications, it is of upmost importance to reduce the mass of the fuel cell system. A considerable amount of the total system mass is due to the fuel cell cooling system. This paper discusses two-phase cooling of the bipolar plates in a fuel cell stack. A twostep approach was used. First, test were carried out with machined plates with a geometry similar to bipolar plates. In a second step, actual bipolar plates were tested, including the membranes, seals and gaskets that are present in a fuel cell. These bipolar plates are from a standard fuel cell stack and are intended for water/glycol cooling. The tests show that these standard bipolar plates can be used with two-phase cooling with methanol
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ItemInterference Detection, Localization, and Mitigation Capabilities of Controlled Reception Pattern Antenna for Aviation(MDPI, 2023)Global Navigation Satellite System (GNSS) interference poses an increasing threat for civil aviation, and the detection and mitigation of interferences can help to make the sector more robust. This paper focuses on the detection and mitigation capabilities of a software-based Controlled Reception Pattern Antenna (CRPA) as part of a wider study in which different detection and mitigation methods are tested and compared. The proposed CRPA uses eigenvalue decomposition to determine the weight vector and is combined with MUltiple SIgnal Classification (MUSIC) for detection purposes. Simulations are used to test the software CRPA for its robustness against different types of interference in static and dynamic scenarios. The test method and processing pipeline are described. Initial results show the CRPA algorithm under test is capable of detecting and mitigating different types of interferences, and mitigation can help a receiver to maintain a position velocity time (PVT) solution for higher levels of interference power. Keywords: GNSS; CRPA; MUSIC; jamming; spoofing; detection; mitigation; aviation
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ItemSatellite Navigation Signal Interference Detection and Machine Learning-Based Classification Techniques towards Product Implementation(MDPI, 2023)Many critical applications highly depend on Global Navigation Satellite Systems (GNSS) for precise and continuously available positioning and timing information. To warn a GNSS user that the signals are compromised, real-time interference detection is required. Additionally, real-time classification of the interference signal allows the user to select the most effective mitigation methods for the encountered disturbance. A compact proof of concept has been built using commercial off-the-shelf (COTS) components to analyse the jamming detection and classification techniques. It continuously monitors GNSS frequency bands and generates warnings to the user when interference is detected and classified. Various signal spectrum analyses, consisting of kurtosis and power spectral density (PSD) calculations, as well as a machine learning model, are used to detect and classify anomalies in the incoming signals. The system has been tested by making use of a COTS GNSS signal simulator. The simulator is used to generate the upper L-band GNSS signals and different types of interferences. Successful detection and classification is demonstrated, even for interference power levels that do not degrade the performance of a commercial reference receiver.