[1] Duan H B, Qiu H X, Fan Y M. Unmanned aerial vehicle close formation cooperative control based on predatory escaping pigeon-inspired optimization. Sci Sin-Tech, 2015, 45: 559-572 CrossRef Google Scholar
[2] Duan H B, Luo Q N, Yu Y X．Trophallaxis network control approach to formation flight of multiple unmanned aerial vehicles．Sci China Tech Sci, 2013, 56: 1066--1074. Google Scholar
[3] Shen L C, Niu Y F, Zhu H Y. Theories and Methods of Autonomous Cooperative Control for Multiple UAVs. 2nd ed. Beijing: National Defense Industry Press, 2018. Google Scholar
[4] Schmitt F, Schulte A. Mixed-initiative mission planning using planning strategy models in military manned-unmanned teaming missions. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 2015. 1391--1396. Google Scholar
[5] Lim Y, Ramasamy S, Gardi A. Cognitive Human-Machine Interfaces and Interactions for Unmanned Aircraft. J Intell Robot Syst, 2018, 91: 755-774 CrossRef Google Scholar
[6] Dai F, Chen M, Wei X L, et al. Swarm intelligence-inspired autonomous flocking control in UAV networks. IEEE Access, 2019. doi: 10.1109/ACCESS.2019.2916004. Google Scholar
[7] Yang J H, Kapolka M, Chung T H. Autonomy balancing in a manned-unmanned teaming (MUT) swarm attack. In: Robot Intelligence Technology and Applications. Berlin: Springer, 2013. 561--569. Google Scholar
[8] Duan H B, Shao S, Su B W, et al. New development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle. Sci China Tech Sci, 2010, 53: 2025--2031. Google Scholar
[9] Huang H Q, Bai J Q, Zhou H, et al. Present situation and key technologies of unmanned cooperative operation under intelligent air combat system. Navigation Control, 2019, 18: 15--23. Google Scholar
[10] Yan F, Zhu X, Zhou Z. Real-time task allocation for a heterogeneous multi-UAV simultaneous attack. Sci Sin Inform, 2019, 49: 555-569 CrossRef Google Scholar
[11] Alfeo A L, Cimino M G C A, De Francesco N. Swarm coordination of mini-UAVs for target search using imperfect sensors. IDT, 2018, 12: 149-162 CrossRef Google Scholar
[12] Varela G, Caamamo P, Orjales F, et al. Swarm intelligence based approach for real time UAV team coordination in search operations. In: Proceedings of the 3rd World Congress on Nature and Biologically Inspired Computing, 2011. 365--370. Google Scholar
[13] Gao J S, Yu F, Ji X G. Current situation of studies on autonomous control level of UAVs. Electron Opt Control, 2009, 16: 51--54. Google Scholar
Figure 1
(Color online) Air combat simulation system
Figure 2
(Color online) Anjian launching a missile
Figure 3
Flow chart of ant colony optimization
Figure 4
(Color online) Trend of optimum individual fitness
Figure 5
(Color online) UAV path planning
Figure 6
ACL in Unmanned Aircraft System Roadmap 2005$\sim$2030
Figure 7
UAV state machine
Figure 8
(Color online) Battle trajectory
Figure 9
(Color online) Multi-UAV cooperative reconnaissance
Figure 10
(Color online) Multi-UAV cooperative attack
