Identifying Anomalous Behavior in Aircraft Landing Trajectory Using a Bayesian Autoencoder

Abstract

Anomalous behavior during the aircraft landing phase can significantly increase the probability of adverse events. Automated anomaly detection during the landing phase can help aviation safety-related organizations to efficiently detect anomalous behavior and consider mitigation strategies. This paper develops a Bayesian autoencoder neural network model to identify anomalous behavior in landing trajectories by reconstructing the flight data because the reconstruction error is larger for anomalous flights. Different loss functions, such as Huber loss, mean squared error loss, and least trimmed squares are investigated to construct the Bayesian autoencoder model; and their performances are compared using different measures: the mean of the reconstruction error, the standard deviation of the reconstruction error, and both the mean and standard deviation of the reconstruction error. Different loss function-based models show differences in performance, depending on which measure is used for anomaly detection; among all the options considered, one of the Huber loss options appears to give the best performance, as indicated by the F1 score. Furthermore, the mean and standard deviation of the reconstruction error for a single flight are used to identify the time of occurrence and the flight parameters related to anomalous behavior.