Maneuver Decision of Autonomous Air Combat of Unmanned Combat Aerial Vehicle Based on Deep Neural Network

doi:10.3969/j.issn.1000-1093.2020.08.016

Abstract

Abstract: Maneuver decision is a critical factor which determines the success and failure of air combat for unmanned combat aerial vehicle. In order to increase the probability of wining air combat, a deep neural network (DNN) is proposed for maneuver decision. 36 kinds of maneuvers were constructed, and the samples of current situation, control quantity and future situation were acquired through flight simulations. The DNN is trained with the samples, making it capable of predicting future situation according to current information. Decision target function and situation assessment function were designed. In the process of air combat, the trained DNN is used to predict the future situations corresponding to all maneuvers, and the best maneuver is selected from all the maneuvers according to decision target function. The enemy planes which simply and autonomously maneuver were simulated, respectively, under different initial conditions, and the air combat situations were also assessed. The results show that the proposed decision method can be used to win air combat with less actions at balanced situation, and gain an edge through a series of actions at adverse situation, and the decision-making time is reduced by 9 ms.

Key words: unmannedcombataerialvehicle, maneuverdecision, deepneuralnetwork, aircombatsituation, flightsimulation

CLC Number:

E844

ZHANG Hongpeng， HUANG Changqiang， XUAN Yongbo， TANG Shangqin. Maneuver Decision of Autonomous Air Combat of Unmanned Combat Aerial Vehicle Based on Deep Neural Network[J]. Acta Armamentarii, 2020, 41(8): 1613-1622.

References

［1］ AUSTIN F, CARBONE G, FALCO M, et al. Game theory for automated maneuvering during air-to-air combat[J]. Journal of Guidance, 1990, 13(6):1143-1149.
[2] VIRTANEN K, KARELAHATI J, RAIVIO T. Modeling air combat by a moving horizon influence diagram game[J]. Journal of Guidance,Control, and Dynamics, 2006, 29(5):1080-1091.
[3] 张涛, 于雷, 周中良, 等. 基于混合算法的空战机动决策[J]. 系统工程与电子技术, 2013, 35(7):1445-1450.
ZHANG T, YU L, ZHOU Z L, et al. Decision-making for air combat maneuvering based on hybrid algorithm[J]. Systems Engineering and Electronics, 2013, 35(7):1445-1450.(in Chinese)
[4] DUAN H B, ZHANG Y P, LIU S Q. Multiple UAVs/UGVs heterogeneous coordinated technique based on receding horizon control and velocity vector control[J]. Science China, 2011, 54(4):869-876.
[5] ZHANG B, GAO X G, RU W. Route planning of UCAV based on artificial potential field method[C]∥Proceedings of the International Conference on Management Science and Intelligent Control. Honolulu, HI, US: IEEE, 2011:99-102.
[6] LI Y, BAI B D, ZHANG Y N. Improved particle swarm optimization algorithm for fuzzy multi-class SVM[J].Journal of Sysytems Engineering and Electronics, 2010, 21(3):509-513.
[7] 傅莉, 谢怀福, 孟光磊, 等. 基于滚动时域的无人机空战决策专家系统[J]. 北京航空航天大学学报, 2015, 41(11):1994-1999.
FU L, XIE H F, MENG G L, et al. An UAV air-combat decision expert system based on receding horizon control[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(11): 1994-1999.(in Chinese)
[8] HUANG C Q, DONG K S, HUANG H Q, et al. Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization[J]. Journal of Systems Engineering and Electronics, 2018, 29(1):86-97.
[9] 王昱, 章卫国, 傅莉, 等. 基于鲁棒优化的无人机空战博弈决策[J]. 系统工程与电子技术, 2015, 37(11):2531-2535.
WANG Y, ZHANG W G, FU L, et al. Game decision making of UAV aerial combat based on robust optimization[J]. Systems Engineering and Electronics, 2015, 37(11):2531-2535.(in Chinese)
[10] 徐鸣, 马龙华, 顾江萍, 等. 一种改进的多目标鲁棒优化方法[J]. 控制与决策, 2013, 28(8):1178-1182.
XU M, MA L H, GU J P, et al. An improved robust multi-objective optimization control method[J]. Control and Decision, 2013, 28(8):1178-1182.(in Chinese)
[11] 国海峰, 侯满义, 张庆杰, 等. 基于统计学原理的无人作战飞机鲁棒机动决策[J]. 兵工学报, 2017, 38(1):160-167.
GUO H F, HOU M Y, ZHANG Q J, et al. UCAV robust maneuver decision based on statistics principle[J]. Acta Armamentarii, 2017, 38(1):160-167.(in Chinese)
[12] SUN T Y, TSAI S J, LEE Y N, et al. The study on intelligent advanced fighter air combat decision support system[C]∥Proceedings of the International Conference on Information Reuse and Integration. Waikoloa, HI, US: IEEE, 2006:39-44.
[13] 孙楚, 赵辉, 王渊, 等. 基于强化学习的无人机自主机动决策方法[J]. 火力与指挥控制, 2019, 44(4):142-149.
SUN C, ZHAO H, WANG Y, et al. UCAV autonomic maneuver decision- making method based on reinforcement learning[J]. Fire Control and Command Control, 2019, 44(4):142-149.(in Chinese)
[14] 杜海文, 崔明朗, 韩统, 等. 基于多目标优化与强化学习的空战机动决策[J]. 北京航空航天大学学报, 2018, 44(11):2247-2256.
DU H W, CUI M L, HAN T, et al. Maneuvering decision in air combat based on multi-objective optimization and reinforcement learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(11):2247-2256.(in Chinese)
[15] MIRJALILI S, SAREMI S, MIRJALILI S M, et al. Multi-objective grey wolf optimizer:a novel algorithm for multi-criterion optimization[J]. Expert Systems with Applications, 2016, 47:106-119.
[16] 左磊. 基于值函数逼近与状态空间分解的增强学习方法研究[D]. 长沙:国防科学技术大学, 2011.
ZUO L. Research on reinforcement learning based on value function approximation and state space decomposition[D]. Changsha:National University of Defense Technology, 2011.(in Chinese)
[17] 李世豪, 丁勇, 高振龙. 基于直觉模糊博弈的无人机空战机动决策[J]. 系统工程与电子技术, 2019, 41(5):1063-1070.
LI S H, DING Y, GAO Z L. UAV air combat maneuvering decision based on intuitionistic fuzzy game theory[J]. Systems Engineering and Electronics, 2019, 41(5):1063-1070.(in Chinese)
[18] 伊恩·古德费洛, 约书亚·本吉奥，亚伦·库维尔. 深度学习[M].赵申剑，黎彧君，符天凡，等，译. 北京：人民邮电出版社, 2017:126-188.
GOODFELLOW I，BENGIO Y, COURVILLE A. Deep learning[M]. ZHAO S J, LI Y J, FU T F, et al, translated. Beijing: Press of People’s Post and Telecommunications, 2017:126-188.(in Chinese)
[19] WILLIAMS P. Three-dimensional aircraft terrain-following via real-time optimal control[J]. Journal of Guidance, Control, and Dynamics, 1990, 13(6): 1146-1149.
[20] FRED A, GIRO C, MICHAEL F, et al. Automated maneuvering decisions for air-to-air combat[C]∥Proceedings of the Gui-dance, Navigation and Control Conference. Monterey, CA, US: AIAA, 1987:659-669.
[21] 韩立群.人工神经网络[M]. 北京：北京邮电大学出版社, 2006:51-66.
HAN L Q. Artificial neural network[M]. Beijing: Beijing University of Posts and Telecommunications Press, 2006:51-66.(in Chinese)
[22] HE K M, ZHANG X Y, REN S Q, et al. Delving deep into rectifiers:surpassing human-level performance on imagenet classification[C]∥Proceedings of International Conference on Computer Vision. Santiago, Chile:IEEE, 2015:1026-1034.
[23] MCGREW J S, HOW J P, WILLIAMS B. Air-combat strategy using approximate dynamic programming[J]. Journal of Gui-dance, Control, and Dynamics, 2010, 33(5):1641-1654.
[24] SHAW R L. Fighter combat tactics and maneuvering[M]. Annapolis, MD, US: Naval Institute Press, 2007:10-13.

[1]	CUI Lingfei， GUO Yonghong， XIU Quanfa， SHI Chao， ZHANG Shuoyang. UAV Detection Method Based on Domestic Embedded Intelligent Computing Platform [J]. Acta Armamentarii, 2022, 43(S1): 146-154.
[2]	ZHOU Ying， XIE Zhenping， JIANG Xiaojun. Simulation Design of Camouflage Target Concealing Strategy Based on Evolutionary Computation [J]. Acta Armamentarii, 2021, 42(9): 1987-1997.
[3]	YANG Aiwu, LI Zhanwu, LI Bao, XI Zhifei, GAO Chunqing. Air Combat Situation Assessment Based on Dynamic Variable Weight [J]. Acta Armamentarii, 2021, 42(7): 1553-1563.
[4]	ZHANG Hongpeng， HUANG Changqiang， TANG Shangqin， XUAN Yongbo. CNN-based Real-time Prediction Method of Flight Trajectory of Unmanned Combat Aerial Vehicle [J]. Acta Armamentarii, 2020, 41(9): 1894-1903.
[5]	ZHANG Qingbin, GAO Feng, GUO Rui, FENG Zhiwei, GE Jianquan. Multibody Dynamics Modeling of Powered Parafoil System Using Equations with Quasi-coordinates [J]. Acta Armamentarii, 2019, 40(9): 1935-1942.
[6]	LENG Peng-fei， XU Chao-yang. Specific Emitter Identification Based on Deep Reinforcement Learning [J]. Acta Armamentarii, 2018, 39(12): 2420-2426.