State Prediction of High-speed Ballistic Vehicles with Gaussian Process

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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.
Publisher
INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
Issue Date
2018-06
Language
English
Article Type
Article
Keywords

TRACKING

Citation

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.16, no.3, pp.1282 - 1292

ISSN
1598-6446
DOI
10.1007/s12555-016-0552-2
URI
http://hdl.handle.net/10203/244294
Appears in Collection
IE-Journal Papers(저널논문)AE-Journal Papers(저널논문)
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