Integrating Artificial Intelligence with Space-Based ADS-B for Next-Generation Space Traffic Management

This paper explores into the concept of space traffic management using current aircraft navigation and surveillance systems such as ADS-B technologies, along with artificial intelligence (AI). This paper conducts a feasibility study on the use of present ADS-B systems that are modified for space flight and satellite surveillance. The scope of the paper focuses on mainly the surveillance of commercial space flights, as well as sub-orbital space vehicles (such as reusable launch vehicles, satellites in low earth orbit (LEO), etc.), and the potential safety hazards that can be posed. The objective of this study is to build a model to manage space traffic along with commercial air traffic simultaneously, while also focusing on tracking and guiding space traffic, to ensure safe operations. A conceptual view of the model, along with the introduction of ADS-B, how it works and the integration of it, with AI, potential limitations and hazards that can occur and possible solutions to these are explained, and recommendations for future studies is also mentioned. The proposed idea could use the help of the current trends of AI and Machine Learning that will be instrumental in the future for making such models more efficient, reliable and safer. Hence, this paper explores the idea of AI-enhanced ADS-B Systems for Space Traffic. By using AI and Machine Learning to optimize the efficiency of ADS-B, it was found that anomalies could be easily detected quicker [1], safety could be improved, analysis of flight path regions could be automated, etc.