Mechanics & Industry
Volume 23, 2022
|Number of page(s)||6|
|Published online||10 October 2022|
On the anti-missile interception technique of unpowered phase based on data-driven theory
School of Energy and Power Engineering, Nanjing University of Science and Technology,
200 Xiaolingwei Street,
2 School of Automation, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China
* e-mail: firstname.lastname@example.org
Accepted: 18 August 2022
Abstract. The anti-missile interception technique of unpowered phase is of much importance in the military field, which depends on the prediction of the missile trajectory and the establishment of the missile model. With rapid development of data science field and large amounts of available data observed, there are more and more powerful data-driven methods proposed recently in discovering governing equations of complex systems. In this work, we introduce an anti-missile interception technique via a data-driven method based on Koopman operator theory. More specifically, we describe the dynamical model of the missile established by classical mechanics to generate the trajectorial data. Then we perform the data-driven method based on Koopman operator to identify the governing equations for the position and velocity of the missile. Numerical experiments show that the trajectories of the learned model agree well with the ones of the true model. The effectiveness and accuracy of this technique suggest that it will be realized in practical applications of anti-missile interception.
Key words: Anti-missile interception / data-driven modelling / machine learning / Koopman operator
© Y. Huang and Y. Li, Published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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