Enhancing Civil Aviation Surveillance Through Multiple Hypothesis Tracking: A Case Study with TIS-B Traffic

Air traffic surveillance systems are essential for aviation safety, ensuring smooth and efficient air traffic flow. Traditional data association methods, such as Global Nearest Neighbor (GNN), are used for civil aviation surveillance, but incorporating Multiple Hypothesis Tracking (MHT) could greatly improve track continuity and reduces ghost tracks, especially benefiting Traffic Information Service-Broadcast (TIS-B) traffic. This paper analyzes MHT's use in civil aviation. Our research shows that MHT significantly improves measurement association processes, yielding more reliable tracking information. Testing with real-world Radar data confirms MHT's advantages, showing notable improvements in track continuity for general aviation and a decrease in ghost tracks. Case studies highlight MHT's ability to clarify aircraft trajectories, with data supporting its integration into civil aviation. These findings indicate MHT's potential to enhance safety, suggesting a re-evaluation of its role in air traffic surveillance systems.