Computer Science ›› 2021, Vol. 48 ›› Issue (2): 55-63.doi: 10.11896/jsjkx.191100053

• New Distributed Computing Technologies and Systems • Previous Articles     Next Articles

Study on Heterogeneous UAV Formation Defense and Evaluation Strategy

ZUO Jian-kai1, WU Jie-hong1, CHEN Jia-tong2, LIU Ze-yuan3, LI Zhong-zhi1   

  1. 1 School of Computer Science,Shenyang Aerospace University,Shenyang 110136,China
    2 School of Aviation Engine,Shenyang Aerospace University,Shenyang 110136,China
    3 School of Aerospace,Shenyang Aerospace University,Shenyang 110136,China
  • Received:2019-11-07 Revised:2020-01-15 Online:2021-02-15 Published:2021-02-04
  • About author:ZUO Jian-kai,born in 1997,Ph.D student.His main research interests include machine learning,deep learning,pattern recognition and computer vision.
    WU Jie-hong,born in 1971,Ph.D,professor,prominent teacher.Her main research interests include correspondence security,collaborative obstacle avoi-dance,autonomous flocking control and power consumption optimization of unmanned autonomous systems.
  • Supported by:
    The Aeronautical Science Foundation of China(2018ZC54013),Innovative Talents Foundation of Education Department of Lianing Province(2018059) and National Innovation and Entrepreneurship Training Program for College Students(201910143423).

Abstract: The problem of UAV formation confrontation has always been a hot topic in scientific research,and there are few related studies on the deployment of UAV group defense.Based on the protection of defensive UAV against common UAV,such as civil,commercial,reconnaissance,cruise and exploration,the coding and decoding scheme of existing heterogeneous UAV formation is improved.The fitness function is established from the missile flight distance and the safety of unarmed drones,and the genetic algorithm is used to optimize the defense formation of the drone.According to the situation of enemy UAV of different sizes and various formations,the formation of our UAV is optimized.The solution results show that the genetic algorithm can converge to the optimal value in different enemy formations at a high speed within 30 iterations,and the corresponding optimized formation is given.Finally,by evaluating the probability effect and drawing the loss curve of five combat situations,it can be seen that the defense deployment strategy designed in this paper is effective.The maximum loss quantity of our UAVs is 6,minimum loss quantity is 0,average loss quantity is 3,and average loss rate is 18.75%.This method is of great significance for the research of UAV group defense deployment.

Key words: Battle damage assessment, Formation optimizationm, Genetic algorithm, Multi-agent, Swarm defense deployment

CLC Number: 

  • TP181
[1] ZHAO K X,HUANG C Q,WEI Z L,et al.Estimation of Air Combat Situation of UAVs Based on Improved Decision Tree[J].Journal of Harbin Institute of Technology,2019,51(4):66-73.
[2] LI S H,DING Y,GAO Z L.Aircraft maneuver decision making of drone based on intuitionistic fuzzy game[J].System Enginee-ring and Electronics,2019,41(5):1063-1070.
[3] AKOPOV A S,BEKLARYA N L A,THAKUR M,et al.Parallel Multi-agent Real-coded Genetic Algorithm for Large-scale Black-box Single-objective Optimisation[J].Knowledge Based Systems,2019,174:103-122.
[4] ZHA NG K S,WANG Z P.Research on Air Combat Formation Optimization Based on Genetic Simulated Annealing Algorithm[J].Journal of Northwestern Polytechnical University,2003(4):477-480.
[5] XIA Q J,ZHANG A N,ZHANG Y Z.Large-scale formation air combat formation optimization algorithm[J].Control Theory & Applications,2010,27(10):1418-1422.
[6] QIAN B,JIANG C S.Application of Genetic Algorithm in Air Combat Formation Optimization of Helicopter[J].Electronics Optics & Control,2008(1):6-9.
[7] MULGUND S,HARPER K,KRISHNAKUMAR K,et al.Large-scale Air Combat Tactics Optimization Using Genetic Algorithms[J].Journal of Guidance,Control,and Dynamics,2001,24:140-142.
[8] XIONG W,DING Q X,CHEN Z J,et al.Optimal allocationmethod of fleet formation based on genetic fuzzy clustering[J].Journal of Beijing University of Aeronautics and Astronautics,2008(2):193-196,214.
[9] LUO D L,WANG B,GONGH J,et al.Collaborative multi-target attack decision based on SAGA[J].Journal of Harbin Institute of Technology,2007(7):1154-1158.
[10] XING X J,XI A,YAN J G.Study on the Optimal Robust Control Method for Multi-UAV Cooperative Formation[J].Journal of Northwestern Polytechnical University,2013,31(5):722-726.
[11] PU P,ZHANG J C,SUN X J.Research on Tactical Decision-making of Multi-machine Cooperative Multiple Target Assignment[J].Tactical Missile Technology,2007(2):57-61.
[12] ZHANG K S.Research on Air Combat Formation Optimization Based on Genetic Algorithm[D].Shaanxi:Northwestern Polytechnical University,2003.
[13] CHEN W,ZHANG Q M.Preliminary Study on Dynamic Analysis Model of Missile Structure under Explosive Shock Wave[J].Explosion and Shock,2009,29(2):199-204.
[14] NAGATA T.A Multi-agent Based Micro-grid Operation Method Considering Charging and Discharging Strategies of Electric Vehi-cles[J].IEEE Transactions on Power and Energy,2018,138(7):598-604.
[15] SHALASH N A,AHMAD A Z,JABER A S.Multi-agent Approach to Reliability Assessment of Power System Generation Using Fuzzy Logic[J].International Journal of Simulation:Systems,Science and Technology,2016,17(32).
[16] LI C,WANG Y,ZHOU H,et al.Intelligent Decision-making Method for Air Combat Formation of Multi-Unmanned Combat Aircraft[J].Fire Control & Command Control,2018,43(7):26-31.
[17] WANG W,WANG D,PENG Z H.Cooperative Learning Neural Network Output Feedback Control of Uncertain Nonlinear Multi-agent Systems Under Directed Topologies[J].International Journal of Systems Science,2017,48(12):2590-2598.
[18] EZUGWU A E,FRINCU M E,ADEWUMI A O.Neural Network-based Multi-agent Approach for Scheduling in Distributed Systems[J].Concurrency Computation,2017,29(1):10.
[19] JOLLY K G,SREERAMA K R,VIJAYAKUMAR R.Intelli-gent Task Planning and Action Selection of a Mobile Robot in a Multi-agent System through a Fuzzy Neural Network Approach[J].Engineering Applications of Artificial Intelligence,2010,23(6):923-933.
[20] CHANG Y Z,LI Z W,KOU Y X,et al.A Method for Selecting the Formation of Air Combat in Uncertain Information[J].System Engineering and Electronics,2016,38(11):2552-2560.
[21] XIAO B S,FANG Y W,XU Y S.Research on Coordinated Formation Target Assignment Model for beyond Visual Range Air Combat[J].Systems Engineering and Electronics,2010,32(7):1476-1479.
[22] DIAO X H,FANG Y W,XIAO B S,et al.Task Allocation in Cooperative Air Combat Based on Multi-agent Coalition[J].Journal of Beijing University of Aeronautics and Astronautics,2014,40(9):1268-1275.
[23] YE X D,CHEN B,LI P,et al.A Simulation-based Multi-agent Particle Swarm Optimization Approach for Supporting Dynamic Decision Making in Marine Oil Spill Responses[J].Ocean and Coastal Management,2019,172:128-136.
[24] KUMAR R,SHARMA D,SADU A.A Hybrid Multi-agentBased Particle Swarm Optimization Algorithm for Economic Power Dispatch[J].International Journal of Electrical Power and Energy Systems,2011,33(1):115-123.
[25] XU P,XIE G M,WEN J Y,et al.Event-driven reinforcement learning multi-agent formation control[J].Journal of Intelligent Systems,2019,14(1):93-98.
[26] WANG X C.Research on multi-robot collaborative coordination based on reinforcement learning and clustering intelligent met hod[D].Harbin:Harbin Engineering University,2005.
[27] ZHENG Y B,XI P X,WANG L L,et al.Multi-agent formation obstacle avoidance method based on artificial potential field method[J].Journal of Computer Applications,2018,38(12):3380-3384,3413.
[1] SHI Dian-xi, ZHAO Chen-ran, ZHANG Yao-wen, YANG Shao-wu, ZHANG Yong-jun. Adaptive Reward Method for End-to-End Cooperation Based on Multi-agent Reinforcement Learning [J]. Computer Science, 2022, 49(8): 247-256.
[2] YANG Hao-xiong, GAO Jing, SHAO En-lu. Vehicle Routing Problem with Time Window of Takeaway Food ConsideringOne-order-multi-product Order Delivery [J]. Computer Science, 2022, 49(6A): 191-198.
[3] WANG Qi, WANG Gang-qiao, CHEN Yong-qiang, LIU Yi. Integrated Modeling Method and Application System for Social Computing [J]. Computer Science, 2022, 49(4): 25-29.
[4] SHEN Biao, SHEN Li-wei, LI Yi. Dynamic Task Scheduling Method for Space Crowdsourcing [J]. Computer Science, 2022, 49(2): 231-240.
[5] WU Shan-jie, WANG Xin. Prediction of Tectonic Coal Thickness Based on AGA-DBSCAN Optimized RBF Neural Networks [J]. Computer Science, 2021, 48(7): 308-315.
[6] WANG Jin-heng, SHAN Zhi-long, TAN Han-song, WANG Yu-lin. Network Security Situation Assessment Based on Genetic Optimized PNN Neural Network [J]. Computer Science, 2021, 48(6): 338-342.
[7] ZHENG Zeng-qian, WANG Kun, ZHAO Tao, JIANG Wei, MENG Li-min. Load Balancing Mechanism for Bandwidth and Time-delay Constrained Streaming Media Server Cluster [J]. Computer Science, 2021, 48(6): 261-267.
[8] GAO Feng-yue, WANG Yan, ZHU Tie-lan. Resilient Distributed State Estimation Algorithm [J]. Computer Science, 2021, 48(5): 308-312.
[9] YAO Ze-wei, LIU Jia-wen, HU Jun-qin, CHEN Xing. PSO-GA Based Approach to Multi-edge Load Balancing [J]. Computer Science, 2021, 48(11A): 456-463.
[10] GAO Shuai, XIA Liang-bin, SHENG Liang, DU Hong-liang, YUAN Yuan, HAN He-tong. Spatial Cylinder Fitting Based on Projection Roundness and Genetic Algorithm [J]. Computer Science, 2021, 48(11A): 166-169.
[11] GAO Ji-xu, WANG Jun. Multi-edge Collaborative Computing Unloading Scheme Based on Genetic Algorithm [J]. Computer Science, 2021, 48(1): 72-80.
[12] JI Shun-hui, ZHANG Peng-cheng. Test Case Generation Approach for Data Flow Based on Dominance Relations [J]. Computer Science, 2020, 47(9): 40-46.
[13] DONG Ming-gang, HUANG Yu-yang, JING Chao. K-Nearest Neighbor Classification Training Set Optimization Method Based on Genetic Instance and Feature Selection [J]. Computer Science, 2020, 47(8): 178-184.
[14] LIANG Zheng-you, HE Jing-lin, SUN Yu. Three-dimensional Convolutional Neural Network Evolution Method for Facial Micro-expression Auto-recognition [J]. Computer Science, 2020, 47(8): 227-232.
[15] YANG De-cheng, LI Feng-qi, WANG Yi, WANG Sheng-fa, YIN Hui-shu. Intelligent 3D Printing Path Planning Algorithm [J]. Computer Science, 2020, 47(8): 267-271.
Full text



No Suggested Reading articles found!