Reactive Temporal Logic-Based Precursor Detection Algorithm for Terminal Airspace Operations

Abstract

The air traffic management system is one of the most complex manmade systems, with stringent standards for safety and operational performance. Modern surveillance systems make available detailed flight and airport information through onboard and ground recording systems. These recorded datasets can be used for detecting and/or predicting anomalies that hinder safe and efficient operations. The prediction of an anomaly is performed by identifying events that precede the occurrence of an anomaly, which are called precursors. In this paper, we propose a precursor detection algorithm that can identify precursors for flight anomalies through data-driven models designed with surveillance data recorded in the terminal airspace. The proposed algorithm is demonstrated to detect precursors of flight anomalies in the terminal airspace around LaGuardia Airport in New York City using real traffic data obtained from the Airport Surface Detection Equipment–Model X and the terminal automation information service surveillance datasets.