A decision support system for multi-domain air and space traffic management

Air and space transportation systems are rapidly evolving, impacting both atmospheric and space flight domains. These evolutions include the expansion of Air Traffic Management (ATM) beyond its conventional boundaries, to include both Low- and High-Altitude Operations (LAO/HAO). The adoption of new aerospace vehicles and associated transportation modes engenders a complex airspace management framework with increasing challenges in terms of safety, operational efficiencies and environmental sustainability. The existing fragmentation of airspace management services exacerbates difficulties in decision-making due to restricted situational awareness, communication deficiencies, and insufficient data analysis tools applicable across several domains. To address these challenges, a Decision Support System (DSS) is proposed that addresses the requirements of an integrated Multi-Domain Traffic Management (MDTM) network. The proposed DSS aims to improve the decision-making abilities of those involved in the conventional aviation, LAO/HAO and point-to-point suborbital spaceflight. This system aims to increase efficiency, safety, and sustainability. The framework utilizes the principles of data-driven decision-making, real-time information interchange, and advanced optimization algorithms. It consolidates essential MDTM data sources, such as satellite-based tracking and aircraft/HAO operational data, into a centralized data repository. The system utilizes sophisticated data processing methods to do predictive analysis and evaluate risks, offering decision-makers practical information regarding probable conflicts, optimal routing, and hazards. This paper introduces an innovative architecture for an MDTM DSS, which serves as a basis for further investigation and advancement in this critical field. Simulation case studies were conducted to validate the efficacy of the MDTM DSS algorithms in generating conflict-free trajectories and optimizing object tracking in real time using a standard PC platform. The results from the three case studies collectively underscore the MDTM DSS framework's robust capability to address complex challenges in multi-domain transportation systems. Together, these findings confirm the DSS potential to generate smooth Uncertainty Volumes (UV), human response latency, Separation Assurance and Collision Avoidance (SACA) within the MDTM, demonstrating its capability to handle trajectory prediction under uncertainty. This significantly enhances operational safety, efficiency, and sustainability in both air and space domains by integrating advanced algorithms, real-time data processing, and human-machine interactions.