Seonggyun Lee  Joonseong Kang  Jeyoon Yeom  Dongwg Hong  Youngmin Kim  Kyungwoo Song

10.5626/JOK.2025.52.3.241

trajectory prediction for flight vehicles multivariate time-series forecasting deep generative model conditional diffusion model

This paper proposes a new model for processing multivariate time series data aimed at predicting nonlinear trajectories related to aircraft pull-up maneuvers. To achieve this, aircraft trajectories were predicted using CSDI (Conditional Score-based Diffusion Models for Imputation), a state-of-the-art generative AI model. Specifically, because the flight distance and shape of the aircraft vary significantly depending on the presence of pull-up maneuvers, the data were separated into subsets with and without these maneuvers to train and predict distinct models. Experimental results demonstrated that the model predicted trajectories very similar to actual trajectories and achieved superior performance in MAE, RMSE, and CRPS metrics compared to existing deep learning models. This study not only enhances the accuracy of aircraft trajectory prediction but also suggests the potential for more sophisticated predictions through future integration with Classifier Diffusion models.