Transactions of Nanjing University of Aeronautics & Astronautics
An Aircraft Trajectory Anomaly Detection Method Based on Deep Mixture Density Network
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Abstract:
The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety. However， the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories. Low anomaly detection accuracy still exists due to the high-dimensionality， heterogeneity and temporality of flight trajectory data. To this end， this paper proposes an abnormal trajectory detection method based on the deep mixture density network （DMDN） to detect flights with unusual data patterns and evaluate flight trajectory safety. The technique consists of two components： Utilization of the deep long short-term memory （LSTM） network to encode features of flight trajectories effectively， and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model （GMM）. Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories. The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods. The proposed model can be used as an assistant decision-making tool for air traffic controllers.
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Project Supported:
This work was supported in part by the National Natural Science Foundation of China （Nos. 62076126， 52075031）， and the Postgraduate Research & Practice Innovation Program of Jiangsu Province （No. SJCX19_0013）.
Clc Number:
V19
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