Research on missile avoidance decision training based on improved DDPG algorithm

Xin-Lei Fan; Jie Zou; Peng-fei Wang; Kai Liu

doi:10.1117/12.2586343

12 March 2021 Research on missile avoidance decision training based on improved DDPG algorithm

Xin-Lei Fan, Jie Zou, Peng-fei Wang, Kai Liu

Proceedings Volume 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications; 117632A (2021) https://doi.org/10.1117/12.2586343
Event: Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, 2020, Kunming, China

Abstract

Aiming at the problem of autonomous evasion of carrier aircraft facing enemy incoming missiles, a deep reinforcement learning method based on the improved DDPG algorithm is adopted to train and learn the problem, and in addition to considering the evasion performance in the reward function, it also focuses on the original The aircraft's altitude maintenance and speed maintenance, as well as the relative altitude change and approach speed change of the incoming missile, are used to establish a reward model. Finally, a training simulation test analysis was carried out based on the aircraft model. Through simulation, it can be seen that the training results can effectively realize the evasion decision of the incoming missile, and the designed reward function and input parameters can also play a correct role, and the results are available Certain generalization ability.

Citation Download Citation

Xin-Lei Fan, Jie Zou, Peng-fei Wang, and Kai Liu "Research on missile avoidance decision training based on improved DDPG algorithm", Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 117632A (12 March 2021); https://doi.org/10.1117/12.2586343

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available