无人机蜂群电磁作战概念仿真

doi:10.3969/j.issn.1001-506X.2020.07.12

系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (7): 1510-1518.doi: 10.3969/j.issn.1001-506X.2020.07.12

无人机蜂群电磁作战概念仿真

张阳^1,^2,^3,*(), 司光亚²(), 王艳正²()

1. 国防大学研究生院, 北京 100091
2. 国防大学联合作战学院, 北京 100091
3. 中国洛阳电子装备试验中心, 河南洛阳 471003

收稿日期:2019-09-19 出版日期:2020-06-30 发布日期:2020-06-30
通讯作者: 张阳 E-mail:361477421@qq.com;Sgy863@163.com;wyzcyber@263.com
作者简介:司光亚(1967-),男,教授,博士研究生导师,博士,主要研究方向为计算机战争模拟。E-mail:Sgy863@163.com|王艳正(1976-),男,高级工程师,博士,主要研究方向为计算机战争模拟。E-mail:wyzcyber@263.com
基金资助:
国家自然科学基金(U1435218);装备预研共用技术课题(41401050103)

Simulation of unmanned aerial vehicle swarm electromagnetic operation concept

Yang ZHANG^1,^2,^3,*(), Guangya SI²(), Yanzheng WANG²()

1. Graduate School, National Defence University, Beijing 100091, China
2. Joint Operations College, National Defence University, Beijing 100091, China
3. Luoyang Electronic Equipment Test Center, Luoyang 471003, China

Received:2019-09-19 Online:2020-06-30 Published:2020-06-30
Contact: Yang ZHANG E-mail:361477421@qq.com;Sgy863@163.com;wyzcyber@263.com
Supported by:
国家自然科学基金(U1435218);装备预研共用技术课题(41401050103)

摘要/Abstract

摘要：

首先，提出了基于四环聚焦的无人机(unmanned aerial vehicle, UAV)蜂群电磁作战概念设计框架,并以彻底毁瘫防空体系为作战目标,设计“蜂刺”作战概念及“雷电蜂”作战行动。然后,面向体系对抗仿真的需求,提出了UAV蜂群电磁作战行动实体蜂群行为网络化交互(enity swarm behavior networked-interaction, ESBNI)建模框架,并研究了基于分层描述的UAV蜂群力量实体建模方法,以及基于行为规则的UAV蜂群电磁行为建模方法。再次,依托UAV蜂群电磁作战仿真原型(electromagnetic swarm operation simulation, ESS)系统,开展了“雷电蜂”作战行动的演示。最后,通过多案比对探索性仿真的方法,评估了联合作战背景下的UAV蜂群电磁作战效能,并验证了该作战概念的合理性、有效性。

关键词: 无人机蜂群, 电磁作战, 作战概念, 建模与仿真

Abstract:

First, the four-ring focus design framework of the unmanned aerial vehicle (UAV) swarm electromagnetic operation is proposed. Then, the swarm spur operation concept and the thunder swarm operation are designed with the goal of completely destroying the air defense system. Then, for the requirements of system-of-systems (SoS) confrontation simulation, the enity swarm behavior networked-interaction (ESBNI) modeling framework for the UAVs swarm electromagnetic operation is established, the method of modeling the UAVs swarm entity based on hierarchical description is studied, and the method of modeling the UAVs swarm cyber behavior is given. Thirdly, relying on the electromagnetic swarm operation simulation (ESS) system, a demonstration of thunder swarm operation is carried out. Finally, through the multi-case comparison exploratory simulation method, the performance of the UAVs swarm electromagnetic operation in the joint operation background is evaluated, and the rationality and effectiveness of the operation concept are verified.

Key words: unmanned aerial vehicle (UAV) swarm, electromagnetic operation, operation concept, modeling and simulation

中图分类号:

TP399

张阳, 司光亚, 王艳正. 无人机蜂群电磁作战概念仿真[J]. 系统工程与电子技术, 2020, 42(7): 1510-1518.

Yang ZHANG, Guangya SI, Yanzheng WANG. Simulation of unmanned aerial vehicle swarm electromagnetic operation concept[J]. Systems Engineering and Electronics, 2020, 42(7): 1510-1518.

图/表 15

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

表1

图13

图14

参考文献 30

1	胡晓峰. 战争工程论——走向信息时代的战争方法学[M]. 北京: 科学出版社, 2018.
	HU X F . On war engineering-war methodology towards the information age[M]. Beijing: Science Press, 2018.
2	PHAM L . UAV swarm attack:protection system alternatives for destroyers[J]. Monterey, California, USA: Navy Postgraduate School, 2012, 12, 120- 130.
3	CHUNG T H, JONES K D, DAY M A. 50 vs. 50 by 2015: swarm vs. swarm UAV live-fly competition at the naval postgraduate school[R]. Monterey, California, USA: Navy Postgraduate School, 2015, 3: 1-10.
4	张敏. 智能战争时代,谁来开火?[J]. 军事文摘, 2017, 21, 23- 26.
	ZHANG M . Who is going to fire in the era of intelligent warfare?[J]. Military Digest, 2017, 21, 23- 26.
5	杨镜宇, 胡晓峰. 基于体系仿真试验床的新质作战能力评估[J]. 军事运筹与系统工程, 2016, 30 (3): 5- 9. doi: 10.3969/j.issn.1672-8211.2016.03.001
	YANG J Y , HU X F . New type operation evaluation based on SoS simulation test bed[J]. Military Operations Research and Systems Engineering, 2016, 30 (3): 5- 9. doi: 10.3969/j.issn.1672-8211.2016.03.001
6	RABAH M , ROHAN A , TALHA M , et al. Autonomous vision-based target detection and safe landing for UAV[J]. International Journal of Control Automation and Systems, 2018, 16 (6): 3013- 3025. doi: 10.1007/s12555-018-0017-x
7	BRYAN C , MARK G . Winning in the gray zone-using electromagnetic warfare to regain escalation dominance[J]. Washington, DC, USA: Center for Strategic and Budgetary Assessments (CSBA), 2017, 10, 30- 32.
8	HUANG S , TEO R S H . Distributed UAV loss detection and auto-replacement protocol with guaranteed properties[J]. Journal of Intelligent & Robotic Systems Theory & Applications, 2019, 93 (1/2): 303- 316.
9	JOHN K . Regaining the advantage cognitive electronic warfare[J]. The Journal of Electronic Defense, 2016, 12, 56- 63.
10	OSNER N R , DU P W P . Threat evaluation and jamming allocation[J]. IET Radar, Sonar & Navigation, 2017, 11 (3): 459- 465.
11	OSNER N, PLESSIS W. Electronic warfare training applications of decision-support systems[C]//Proc.of the Defense Operation Application, 2017: 130-135.
12	ROSALIE M, DANOY G. UAV multilevel swarms for situation management[C]//Proc.of the 16th DroNet, 2016: 49-52.
13	ZHANG S Z . Swarm intelligence applied in green logistics: a literature review[J]. Engineering Application of Artificial Intelligence, 2015, 37 (7): 16- 17.
14	DABKOWSKI M , COOK J , KEWLEY R . Swarming UAS II[J]. West Point, New York, USA: United States Military Academy, Department of Systems Engineering, 2010, 2- 6.
15	LI N , HUAI W Q , WANG S D . The solution of target assignment problem in command and control decision-making behaviour simulation[J]. Enterprise Information Systems, 2016, 11 (7): 1059- 1077.
16	LIU W , GU W , SHENG W X , et al. Decentralized multi-agent system-based cooperative frequency control for autonomous microgrids with communication constraints[J]. IEEE Trans.on Sustainable Energy, 2017, 5 (2): 446- 456.
17	ZBYNEK O. Software environment for simulation of UAV multi-agent system[C]//Proc.of the 21st International Conference on Methods and Models in Automation and Robotics, 2016: 134-139.
18	ZHU X P , LIU Z C , YANG J . Model of collaborative UAV swarm toward coordination and control mechanisms study[J]. Procedia Computer Science, 2015, 51 (5): 493- 502.
19	TIWARI R, JAIN P, BUTAIL S. Effect of leader placement on robotic swarm control[C]//Proc.of the 16th International Conference on Autonomous Agents and Multiagent Systems, 2017: 1387-1392.
20	GILES C K . A framework for integrating the development of swarm unmanned aerial system doctrine and design[J]. Monterey, California, USA: Department of Systems Engineering Naval Postgraduate School, 2017, 9- 10.
21	DOMENICO P, SALVTORE V, ROCCO A. Agent-based design for UAV mission planning[C]//Proc.of the 8th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 2013: 76-83.
22	JENSEN J S, BUCHANAN K, HUFF G H. A computer vision-based framework for the synthesis and analysis of beamforming behavior in swarming intelligent systems[C]//Proc.of the IEEE Radar Conference, 2017: 118-122.
23	WILSON O Q, RODRIGUEZ I J. Leader-follower formation for UAV robot swarm based on fuzzy logic theory[C]//Proc.of the Artificial Intelligence and Soft Computing, 2018: 740-751.
24	司光亚, 王艳正. 网络空间作战建模仿真[M]. 北京: 科学出版社, 2018.
	SI G Y , WANG Y Z . Modeling and simulation of cyberspace operation[M]. Beijing: Science Press, 2018.
25	PENG G, FANG Y W, CHEN S H. A hybrid multi-objective discrete particle swarm optimization algorithm for cooperative air combat DWTA[C]//Proc.of the 11st Bio-Inspired Computing-Theories and Applications, 2016: 114-119.
26	YANG J , LIU J . Influence maximization-cost minimization in social networks based on a multiobjective discrete particle swarm optimization algorithm[J]. IEEE Access, 2018, 99 (6): 2320- 2329.
27	ALYSSA P, DANIELA R. Distributed target tracking in cluttered environments with guaranteed collision avoidance[C]//Proc.of the International Symposium on Multi-robot & Multi-agent Systems, 2017: 83-89.
28	FANG Q , XU Q . 3D route planning for UAV based on improved PSO algorithm[J]. Journal of Northwestern Polytechnical University, 2017, 35 (1): 66- 73.
29	SARA P, JULIAN B, EVA B. A multi-UAV minimum time search planner based on ACOR[C]//Proc.of the Genetic and Evolutionary Computation Conference, 2017: 35-42.
30	戴浩. 无人机系统的指挥控制[J]. 指挥与控制学报, 2016, 2 (1): 5- 8.
	DAI H . Command and control for unmanned aerial vehicles[J]. Journal of Command and Control, 2016, 2 (1): 5- 8.

想定名称	想定概要设计	评估指标
基本想定	W军导弹作战力量根据目标列表,向Q地区预定目标实施火力打击作战行动,未实施任何电磁作战支援行动	1. W军摧毁目标数量 2. L军拦截数量 3.作战成本 4.作战收益 5.作战效费比
对比想定1	W军导弹作战力量根据目标列表,向Q地区目标实施火力打击行动; W军指挥员向预定作战区域派出3架电子战飞机,进行远距离支援干扰行动
对比想定2	W军导弹作战力量根据目标列表,向Q地区目标实施火力打击作战行动,齐射规模为24枚;同时, 指挥员向预定作战区域派出由1架有人机负责指挥控制, 10架认知通信对抗UAV(编组为认知通信对抗UAV分群),以及若干架认知雷达对抗UAV(编组为认知雷达对抗UAV分群)负责具体行动的抗UAV蜂群,实施“雷电蜂”作战行动,近距离干扰支援;各UAV不具备反辐射攻击功能
对比想定3	W军指挥员根据目标列表,向预定作战区域派出由1架有人机负责指挥控制, 10架认知通信对抗 UAV(编组为认知通信对抗UAV分群),以及若干架认知雷达对抗 UAV(编组为认知雷达对抗UAV分群)负责具体行动的电磁对抗UAV蜂群,实施“雷电蜂”作战行动,近距离干扰与反辐射攻击目标;导弹作战力量不实施火力打击行动

[1]	赵太飞, 程敏花, 吕鑫喆, 郑博睿. 无人机蜂群中紫外光隐秘通信能耗均衡路由算法[J]. 系统工程与电子技术, 2021, 43(1): 251-257.
[2]	张阳, 司光亚, 王艳正. 无人机集群网电攻击行动协同目标分配建模[J]. 系统工程与电子技术, 2019, 41(9): 2025-2033.
[3]	雷永林, 朱一凡, 谭跃进, 杨峰, 姚剑. 模型驱动的复杂人机系统过程建模仿真方法[J]. 系统工程与电子技术, 2016, 38(1): 223-231.
[4]	杨继坤, 徐廷学, 丛林虎, 董琪. 基于SEBS-TOMS分层组合框架的导弹武器系统可用性建模与仿真[J]. 系统工程与电子技术, 2015, 37(2): 460-467.
[5]	鞠儒生，杨妹，钟荣华，刘晓铖，周云，黄柯棣. 面向服务的建模与仿真技术研究综述[J]. Journal of Systems Engineering and Electronics, 2013, 35(7): 1539-1546.
[6]	潘星, 尹宝石, 温晓华. 基于DoDAF的装备体系任务建模与仿真[J]. Journal of Systems Engineering and Electronics, 2012, 34(9): 1846-1851.
[7]	王勇, 孙涛, 李小偎, 潘泉. 基于Lanchester方程的作战过程建模及仿真研究[J]. Journal of Systems Engineering and Electronics, 2009, 31(7): 1677-1679.

无人机蜂群电磁作战概念仿真

Simulation of unmanned aerial vehicle swarm electromagnetic operation concept

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 30

相关文章 7

编辑推荐

Metrics

本文评价