FlightKoopman: Deep Koopman for Multi-Dimensional Flight Trajectory Prediction

Multi-dimensional Flight Trajectory Prediction (MFTP) in Flight Operations Quality Assessment (FOQA) refers to the estimation of flight status at the future time, accurate prediction future flight positions, flight attitude and aero-engine monitoring parameters are its goals. Due to differences between flight trajectories and other kinds trajectories and difficult access to data and complex domain knowledge, MFTP in FOQA is much more challenging than Flight Trajectory Prediction (FTP) in Air Traffic Control (ATC) and other trajectory prediction. In this work, a deep Koopman neural operator-based multi-dimensional flight trajectory prediction framework, called Deep Koopman Neural Operator-Based Multi-Dimensional Flight Trajectories Prediction (FlightKoopman), is first proposed to address this challenge. This framework is based on data-driven Koopman theory, enables to construct a prediction model using only data without any prior knowledge, and approximate operator pattern to capture flight maneuver for downstream tasks. The framework recovers the complete state space of the flight dynamics system with Hankle embedding and reconstructs its phase space, and combines a fully connected neural network to generate the observation function of the state space and the approximation matrix of the Koopman operator to obtain an overall model for predicting the evolution. The paper also reveals a virgin dataset Civil Aviation Flight University of China (CAFUC) that could be used for MFTP tasks or other flight trajectory tasks. CAFUC Datasets and code is available at this repository: https://github.com/CAFUC-JJJ/FlightKoopman . Experiments on the real-world dataset demonstrate that FlightKoopman outperforms other baselines.