The increasing integration of Uncrewed Aerial Systems (UAS) into controlled airspace presents significant operational and safety challenges, particularly in conflict detection and resolution (CD&R) for Beyond Visual Line of Sight (BVLOS) operations. Ensuring reliable separation management in U-Space requires robust e-conspicuity solutions that address uncertainties in Communication, Navigation, and Surveillance (CNS) systems. This study evaluates the CERTIFLIGHT UTM Box, an e-conspicuity device designed for General Aviation (GA) and UAS operations, incorporating authenticated GNSS tracking, blockchain-based data integrity, and conflict resolution advisory services. Flight validation tests were conducted using a GA aircraft and a UAS to assess the system's effectiveness in detecting and resolving conflicts under realistic operational conditions. The results indicate that while the UTM Box successfully provided conflict advisories, navigation uncertainties and communication delays exceeding five seconds affected its performance. The study highlights the importance of incorporating CNS system uncertainties into CD&R algorithms to ensure safe separation. Future work will focus on refining conflict resolution strategies, integrating advanced filtering techniques to mitigate sensor noise, and enhancing pilot interface design for improved situational awareness and decision support.

Abstract—The integration of unmanned aerial systems (UASs) into existing airspace is imminent as the concept is maturing. U- Space is a concept in which UASs are allowed to fly alongside general aviation aircraft, requiring all aircraft in the airspace to transmit navigation data using radio and network identification. Nevertheless, a consensus is required for the separation in case of conflict amidst the limitations of communication, navigation, and surveillance (CNS) systems. Update rate and probability depict the uncertainty in the communication systems, while spread around the ground truth in position describes the navigation accuracy. Then, BlueSky, an open-source simulator, produces thousands of conflict detection and resolutions to evaluate the CNS limitation on the intrusion prevention rate and loss of separation severity. This paper concludes that higher update rate, probability, and position accuracy lead to safer conflict resolution, and selecting 50 meters as the radius of the protected zone results in a 25-meter intrusion in the worst case.

With the rapid increase in the use of Unmanned Aerial Systems (UAS) for commercial applications such as medical and parcel delivery, the need for safe airborne separation in airspace has become critical. This paper examines the impact of position uncertainty on autonomous separation methods within U-Space, a European Union initiative for managing drone traffic. The study focuses on evaluating various conflict resolution algorithms—specifically, Modified Voltage Potential (MVP) and Velocity Obstacle (VO) variations—under conditions of navigational uncertainty. Through Monte Carlo simulations using the BlueSky ATM simulator, position uncertainty stemming from Global Navigation Satellite Systems (GNSS) errors is modelled and analysed. The research compares the effectiveness of different conflict resolution strategies in preventing conflicts between UAS, measuring intrusion prevention rates and the closest point of approach during encounters. The results indicate that MVP provides superior performance in handling positional uncertainty, offering more robust conflict resolution capabilities than VO-based methods especially at shallow angles conflict situation. These findings are critical for ensuring the safe integration of UAS into increasingly congested airspace environments, guiding future developments in U-Space operations.