Incremental-Learning-Based Unsupervised Anomaly Detection Algorithm for Terminal Airspace Operations

Abstract

The terminal airspace system is one of the most complex human-made dynamical systems with a continuously evolving structure. Modern aviation technologies in the terminal airspace record detailed flight and airport information, including the states of aircraft and the operating conditions of airports as time series datasets, through on-board and ground-based systems. Detecting anomalies in such datasets is emerging as a key problem to understand air transportation system complexity and behavior. This paper proposes an incremental-learning-based anomaly detection algorithm that generates anomaly detection models from surveillance data. The algorithm is developed in a recursive fashion, such that it uses the overnight recorded data to improve the temporal-logic-based anomaly detection models daily—thus it is termed as an overnight update algorithm. The proposed algorithm is demonstrated with real air traffic surveillance data comprising of arrival flights to LaGuardia, John F. Kennedy, and Newark airports, thereby detecting anomalies for arrivals in the terminal airspace of the New York metroplex. This is followed by some analysis and insights from the results of these extensive tests.