Deep Embedding Koopman Neural Operator-Based Nonlinear Flight Training Trajectory Prediction Approach

Jing Lu (), Jingjun Jiang and Yidan Bai
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Jing Lu: College of Computer Science, Civil Aviation Flight University of China, Nanchang Road, Guanghan 618307, China
Jingjun Jiang: College of Computer Science, Civil Aviation Flight University of China, Nanchang Road, Guanghan 618307, China
Yidan Bai: College of Computer Science, Civil Aviation Flight University of China, Nanchang Road, Guanghan 618307, China

Mathematics, 2024, vol. 12, issue 14, 1-20

Abstract: Accurate flight training trajectory prediction is a key task in automatic flight maneuver evaluation and flight operations quality assurance (FOQA), which is crucial for pilot training and aviation safety management. The task is extremely challenging due to the nonlinear chaos of trajectories, the unconstrained airspace maps, and the randomization of driving patterns. In this work, a deep learning model based on data-driven modern koopman operator theory and dynamical system identification is proposed. The model does not require the manual selection of dictionaries and can automatically generate augmentation functions to achieve nonlinear trajectory space mapping. The model combines stacked neural networks to create a scalable depth approximator for approximating the finite-dimensional Koopman operator. In addition, the model uses finite-dimensional operator evolution to achieve end-to-end adaptive prediction. In particular, the model can gain some physical interpretability through operator visualization and generative dictionary functions, which can be used for downstream pattern recognition and anomaly detection tasks. Experiments show that the model performs well, particularly on flight training trajectory datasets.

Keywords: Koopman neural operator; nonlinear trajectories; flight trajectory prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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