State Prediction of High-speed Ballistic Vehicles with Gaussian Process

Il Chul Moon, Kyungwoo Song, Sang Hyeon Kim, Han Lim Choi

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


This paper proposes a new method of predicting the future state of a ballistic target trajectory. There have been a number of estimation methods that utilize the variations of Kalman filters, and the prediction of the future states followed the simple propagations of the target dynamic equations. However, these simple propagations suffered from no observation of the future state, so this propagation could not estimate a key parameter of the dynamics equation, such as the ballistic coefficient. We resolved this limitation by applying a data-driven approach to predict the ballistic coefficient. From this learning of the ballistic coefficient, we calculated the future state with the future ballistic parameter that differs over time. Our proposed model shows the better performance than the traditional simple propagation method in this state prediction task. The value of this research could be recognized as an application of machine learning techniques to the aerodynamics domains. Our framework suggests how to maximize the synergy by linking the traditional filtering aproaches and diverse machine learning techniques, i.e., Gaussian process regression, support vector regression and regularized linear regression.

Original languageEnglish
Pages (from-to)1282-1292
Number of pages11
JournalInternational Journal of Control, Automation and Systems
Issue number3
Publication statusPublished - 2018 Jun 1

Bibliographical note

Publisher Copyright:
© 2018, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications


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