A Clustering-Based Anomaly Detection for Unstable Approach in Terminal Airspace

Data-driven methods are broadly used in analyzing big data in many fields. ADS-B trajectory data provided the possibility of anomaly detection in terminal airspace. Approaching and landing accidents are usually originated from unstable approaches, which are the consequence of go-around failure. Go-around is an aborted landing process initiated in the event of an unsafe landing. In this paper, DBSCAN and HDBSCAN clustering algorithms were employed to detect spatial and energy anomalies using ADS-B trajectory data. Air traffic flow patterns are identified, thus energy safety boundaries are established. Finally, atypical scores are quantified based on the total energy, which serves as the foundation for studying, comprehending, and addressing the factors that cause unstable approach events and go-arounds. This method detects the unstable approach from the spatial and energy perspectives and quantifies the degree of anomaly. It is essential for detecting and predicting anomalies in terminal airspace, and also explores the application of data-driven methods to aviation data.