Investigation on UAV swarm Defense based on electromagnetic intervention control

Author:

Nan Mingyu1,Zhu Yifan1,Wang Tao1,Wang Yanfeng1

Affiliation:

1. College of System Engineering, National University of Defense Technology, China

Publisher

ACM

Reference15 articles.

1. Gu X. Collision-free multiple unmanned combat aerial vehicles cooperative trajectory planning for time-critical missions using differential flatness approach.[J] Defense Sci J 2014;64(1):13–20. Gu X. Collision-free multiple unmanned combat aerial vehicles cooperative trajectory planning for time-critical missions using differential flatness approach.[J] Defense Sci J 2014;64(1):13–20.

2. Heess N. Memory-based control with recurrent neural networks[J] . Computer Science , 2015 . Heess N. Memory-based control with recurrent neural networks[J]. Computer Science, 2015.

3. Hausknecht M. Deep Recurrent Q-Learning for Partially Observable MDPs[J] . Computer Science , 2015 . Hausknecht M. Deep Recurrent Q-Learning for Partially Observable MDPs[J]. Computer Science, 2015.

4. UAV navigation in high dynamic environments: A deep reinforcement learning approach

5. Autonomous navigation of UAV by using real-time model-based reinforcement learning

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