|State Prediction of High-speed Ballistic Vehicles with Gaussian Process
Il-Chul Moon*, Kyungwoo Song, Sang-Hyeon Kim, and Han-Lim Choi
International Journal of Control, Automation, and Systems, vol. 16, no. 3, pp.1282-1292, 2018
Abstract : "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."
Aerodynamics, Gaussian process, high-speed vehicles, state prediction, target trajectory.