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http://dx.doi.org/10.7471/ikeee.2021.25.4.766

A Study on Synthetic Flight Vehicle Trajectory Data Generation Using Time-series Generative Adversarial Network and Its Application to Trajectory Prediction of Flight Vehicles  

Park, In Hee (The 5th R&D Institute - 1st Directorate, Agency for Defense Development)
Lee, Chang Jin (The 5th R&D Institute - 1st Directorate, Agency for Defense Development)
Jung, Chanho (Dept. of Electrical Engineering, Hanbat National University)
Publication Information
Journal of IKEEE / v.25, no.4, 2021 , pp. 766-769 More about this Journal
Abstract
In order to perform tasks such as design, control, optimization, and prediction of flight vehicle trajectories based on machine learning techniques including deep learning, a certain amount of flight vehicle trajectory data is required. However, there are cases in which it is difficult to secure more than a certain amount of flight vehicle trajectory data for various reasons. In such cases, synthetic data generation could be one way to make machine learning possible. In this paper, to explore this possibility, we generated and evaluated synthetic flight vehicle trajectory data using time-series generative adversarial neural network. In addition, various ablation studies (comparative experiments) were performed to explore the possibility of using synthetic data in the aircraft trajectory prediction task. The experimental results presented in this paper are expected to be of practical help to researchers who want to conduct research on the possibility of using synthetic data in the generation of synthetic flight vehicle trajectory data and the work related to flight vehicle trajectories.
Keywords
Generative adversarial network; Synthetic data generation; Trajectory data; Flight vehicles; Trajectory prediction;
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