Computer Science

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).

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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
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