Transactions of Nanjing University of Aeronautics & Astronautics
Identifying Anomaly Aircraft Trajectories in Terminal Areas Based on Deep Auto-encoder and Its Application in Trajectory Clustering
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Abstract:
Anomalous trajectory detection and traffic flow classification for complicated airspace are of vital importance to safety and efficiency analysis. Some researchers employed density-based unsupervised machine learning method to exploit these trajectories related to air traffic control (ATC) actions. However, the quality of position data and the tiny density difference between traffic flows in the terminal area make it particularly challenging. To alleviate these two challenges, this paper proposes a novel framework which combines robust deep auto-encoder (RDAE) model and density peak (DP) clustering algorithm. Specifically, the RDAE model is utilized to reconstruct denoising trajectory and identify anomaly trajectories in the terminal area by two different regularizations. Then, the nonlinear components captured by the encoder of RDAE are input in the DP algorithm to classify the global traffic flows. An experiment on a terminal airspace at Guangzhou Baiyun Airport (ZGGG) with anomaly label shows that the proposed combination can automatically capture non-conventional spatiotemporal traffic patterns in the aircraft movement. The superiority of RDAE and combination are also demonstrated by visualizing and quantitatively evaluating the experimental results.
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TN925
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