Flight Trajectory Prediction Based on Dynamic Mode Decomposition
LU Jing
BAI Yidan
ZHONG Yitao
CHEN Ran
Abstract：Accurate flight trajectory prediction is a key initiative to ensure the safety of flight training.Based on the nonlinear correlation characteristics of subject training data,a combined model of HDMD and Ensemble Kalman filter(EnKF)is proposed to predict the flight trajectory iteratively.Firstly,the flight trajectory data are upgraded to high-dimensional space by HDMD algo-rithm,which retains all its characteristics and obtains an approximate linearized dynamic model of the flight trajectory.On this ba-sis,the dynamic model is adjusted by iteratively updating the EnKF.The experimental results show that the model can effectively re-duce the cumulative error and high-dimensional noise on the problem of influencing the subsequent prediction,and effectively im-prove the prediction accuracy.
Keywords：trajectory predictionnonlinear systemdynamic pattern decompositionEnsemble Kalman filtering
Publication Date：2025-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 61-65,137 )