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.
|Number of pages||11|
|Journal||International Journal of Control, Automation and Systems|
|Publication status||Published - 2018 Jun 1|
Bibliographical noteFunding Information:
Manuscript received September 5, 2016; revised August 8, 2017; accepted October 10, 2017. Recommended by Associate Editor Chang Kyung Ryoo under the direction of Editor Duk-Sun Shim. This work was conducted at High-Speed Vehicle Research Center of KAIST with the support of Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD).
© 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