无人蜂群电磁作战行动建模与仿真

doi:10.12305/j.issn.1001-506X.2023.07.23

摘要/Abstract

摘要：

针对自主、协同、智能的无人蜂群电磁作战行动难以描述、刻画、建模与仿真的问题，首先以联合作战为背景, 分析了无人蜂群电磁作战行动的层次化特征, 并基于BNF (Backus-Naur form)描述了作战行动链。随后, 分析了无人蜂群个体自主作战与群体协同作战的行为特征, 并以此为基础提出了基于RRA (rule, respond and algorithm)的无人蜂群电磁作战行为模型建模方法, 给出建模具体流程与行为规则的形式化描述。再次, 构建了个体电磁辐射源目标自主识别行为模型, 以及群体电磁攻击协同目标分配行为模型, 完成了RRA方法的具体实现。最后, 通过作战仿真实验与算法验证实验, 证明了本文建模思路与建模方法的合理性、有效性。

关键词: 无人蜂群, 建模与仿真, 行为规则

Abstract:

In response to the difficulty in describing, characterizing, modeling, and simulating autonomous, collaborative, and intelligent unmanned aircraft vehicle (UAV) swarm electromagnetic warfare operations, the hierarchical characteristics of UAV swarm electromagnetic warfare operations are analyzed in the context of joint operations, and the combat action chain is described based on the BNF (Backus Naur form) paradigm. Subsequently, the behavioral characteristics of individual autonomous combat and group collaborative combat of UAV swarm colonies are analyzed. Based on this, a modeling method for the electromagnetic warfare behavior model of UAV swarm based on rule, response and algorithm (RRA) is proposed, providing a formal description of the modeling process and behavior rules. Once again, an individual electromagnetic radiation source target autonomous recognition behavior model and a group electromagnetic attack collaborative target allocation behavior model are constructed, and the specific implementation of the RRA method is completed. Finally, through combat simulation experiments and algorithm validation experiments, the rationality and effectiveness of the modeling ideas and methods proposed are demonstrated.

Key words: unmanned aircraft vehicle (UAV) swarm, modeling and simulation, behavior rule

中图分类号:

TM743

张阳, 司光亚, 王艳正, 韩文彬. 无人蜂群电磁作战行动建模与仿真[J]. 系统工程与电子技术, 2023, 45(7): 2121-2130.

Yang ZHANG, Guangya SI, Yanzheng WANG, Wenbin HAN. Modeling and simulation of UAVs swarm electromagnetic operation[J]. Systems Engineering and Electronics, 2023, 45(7): 2121-2130.

图/表 13

表1

图1

表2

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

参考文献 37

1	渊亭防务. 可承担"忠诚僚机"任务的MQ-28A无人机[EB/OL]. [2022-09-30]. https://m.gmw.cn/baijia/2022-09/30/36061328.html.
	Yuan Ting Fang Wu. MQ-28A drone capable of undertaking "loyal wingman" missions[EB/OL]. [2022-09-30]. https://m.gmw.cn/baijia/2022-09/30/36061328.html.
2	DABKOWSKI M M, COOK M J, KEWLEY L R. Swarming UAS Ⅱ[R]. New York: United States Military Academy, 2010: 2-6.
3	OBDRZALEK Z. Software environment for simulation of UAV multi-agent[C]//Proc. of the 21st International Conference on Methods and Models in Automation and Robotics, 2016: 134-139.
4	ZHU XP , LIU Z C , YANG J . Model of collaborative UAV swarm coordination and control mechanisms study[J]. Procedia Computer Science, 2015, 51 (5): 493- 502.
5	ROHAN T, PUNEET J. Effect of leader placement on robotic swarm control[C]//Proc. of the 16th International Conference on Autonomous Agents and Multiagent Systems, 2017: 1387-1392.
6	ZHOU F X , SHEN L J , CHEN B H . Human attentionvolume reconstruction on serial section electron microscopy images[J]. Cytometry Part A, 2021, 99 (6): 575- 585. doi: 10.1002/cyto.a.24332
7	YU X J, CHENG T. Research on a stigmergy-driven & MAS-based method of modeling intelligent system[C]//Proc. of the 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, 2020: 1042-1047.
8	HACHEM J E , CHIPRIANOV V , BABAR M A , et al. Modeling, analyzing and predicting security cascading attacks in smart buildings systems-of-systems[J]. The Journal of Systems and Software, 2020, 162 (4): 110484.
9	SOUZA F, BEZERRA J, HIRATA C M, et al. Combining STPA with SysML Modeling[C]//Proc. of the IEEE International Systems Conference, 2020: 192-200.
10	MARIA R , ATRIA R , MAX T . 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
11	MUHAMMAD S S , AMELIA R . A complex network analysis approach for estimation and detection of traffic incidents based on independent component analysis[J]. Physica A: Statistical Mechanics and its Applications, 2022, 586 doi: 10.1016/j.physa.2021.126504
12	WANG L , DENG T , SHEN Z J M , et al. Digital twin-driven smart supply chain[J]. Frontiers of Engineering Management, 2022, 9 (1): 56- 70. doi: 10.1007/s42524-021-0186-9
13	LI G , ABBASI A , RYAN M J . A simulation-based risk interdependency network model for project risk assessment[J]. Decision Support Systems, 2021, 148 (9): 113602.
14	GUO G , SHAO F J . Mathematical model construction of the production workshop based on the complex network and markov theory[J]. Mathematical Problemsin Engineering, 2021, (26): 1912442.
15	AI J , LIU Y Y , SU Z , et al. K-core decomposition in recommender systems improves accuracy of rating prediction[J]. International Journal of Modern Physics C, 2021, 32 (7) doi: 10.1142/S012918312150087X
16	HAA E S , OBAYA I , AGIZA H N . A fractional complex network model for novel corona virus in China[J]. Advances in Difference Equations, 2021, doi: 10.1186/s13662-020-03182-y
17	YAACOUB E . Travel hopping enabled resource allocation (THEResA) and delay tolerant networking through the use of UAVs in railroad networks[J]. Ad Hoc Networks, 2021, 122 (11): 102628.
18	HUANG Z , WANG H X , ZHANG T B , et al. The collaborative power inspection task allocation method of "unmanned aerial vehicle and operating vehicle"[J]. IEEE Access, 2021, 9, 62926- 62934. doi: 10.1109/ACCESS.2021.3074710
19	ZHU S C , LIN G , ZHAO D M , et al. Learning-based computation offloading approaches in UAVs-assisted edge computing[J]. IEEE Trans. on Vehicular Technology, 2021, 70 (1): 928- 944. doi: 10.1109/TVT.2020.3048938
20	SCHWERD S , SCHULTE A . Operator state estimation to enable adaptive assistancein manned-unmanned-teaming[J]. Cognitive Systems Research, 2021, 67, 73- 83. doi: 10.1016/j.cogsys.2021.01.002
21	张阳, 司光亚, 王艳正. 无人蜂群电磁作战概念仿真研究[J]. 系统工程与电子技术, 2020, 42 (7): 1020- 1030.
	ZHANG Y , SI G Y , WANG Y Z . Simulation of unmanned aerial vehicle swarm electromagnetic operation concept[J]. Systems Engineering and Electronics, 2020, 42 (7): 1020- 1030.
22	KARASAKAL O , KARASAKAL E , SILAV A . A multi-objective approach for dynamic missile allocation using artificial neural networks for time sensitive decisions[J]. Soft Computing, 2021, 25 (6): 5923- 5927.
23	SHI Y C , JIU B , YAN J K , et al. Data-driven radar selection and power allocation method for target tracking in multiple radar system[J]. IEEE Sensors Journal, 2021, 21 (17): 19296- 19306. doi: 10.1109/JSEN.2021.3087747
24	SUN H , LI M , ZHANG L , et al. Resource allocation for multitarget tracking and data reduction in radar network with sensor location uncertainty[J]. IEEE Trans. on Signal Processing, 2021, 69 (2): 4843- 4858.
25	CLARK B, GUNZINGER M, SLOMAN J. Winning in the gray zone-using electromagnetic warfare to regain escalation dominance[R]. Washington, DC: Center for Strategic and Budgetary Assessments, 2017.
26	HUANG S N , TEO R , KWAN J . Distributed UAV loss detection and auto-replacement protocol with guaranteed pro-perties[J]. Journal of Intelligent & Robotic Systems, 2019, 93 (5): 303- 316.
27	BRAIN J. DARPA collaborative operations in denied environment program moves to phase3-UAS vision[EB/OL]. [2021-08-15]. http://www.uasvision.com/2018/11/10/darpas-code-program-moves-phase3.
28	JOHN K . Regaining the advantage cognitive electronic warfare[J]. The Journal of Electronic Defense, 2016, 39 (12): 56- 63.
29	张春磊, 杨小牛. 认知电子战与认知电子战系统研究[J]. 中国电子科学研究院学报, 2014, 9 (6): 551- 555.
	ZHANG C L , YANG X N . Research on the cognitive electronic warfare and cognitive electronic warfare system[J]. Journal of China Academy of Electronics and Information Technology, 2014, 9 (6): 551- 555.
30	LI M C , WANG Z D , LIAO K L , et al. Task allocation on layered multi-agent systems: when evolutionary many-objective optimization meets deep Q-learning[J]. IEEE Trans.on Evolutionary Computation, 2021, 25 (5): 842- 855.
31	HA D T , LILA B , MEGUMI K , et al. Joint beamforming and user association with reduced CSI signaling in mobile environments: a deep Q-learning approach[J]. Computer Networks, 2021, 197 (9): 108291.
32	ZOU G , TANG J F , LEVENT Y , et al. Online food ordering delivery strategies based on deep reinforcement learning[J]. Applied Intelligence, 2021, 52 (6): 6853- 6865.
33	张阳, 司光亚, 王艳正. 体系对抗条件下认知电子攻击行动建模与仿真研究[J]. 中国电子科学研究院学报, 2019, 14 (5): 50- 59.
	ZHANG Y , SI G Y , WANG Y Z . Modeling and simulation of cognitive electronic attack under the condition of system-of-systems combat[J]. Journal of China Academy of Electronics and Information Technology, 2019, 14 (5): 50- 59.
34	赵耀东, 徐旺. 一种基于雷达状态变化的干扰效果在线评估方法[J]. 电子信息对抗技术, 2016, 31 (3): 42- 45.
	ZHAO Y D , XU W . A method of real-time electronic attack effectiveness evaluation based on state transition of radar[J]. Electronic Warfare Technology, 2016, 31 (3): 42- 45.
35	刑强, 贾鑫, 朱卫纲. 无人机群组认知电子战概述及关键技术[J]. 现代防御技术, 2017, 45 (6): 173- 177.
	XING Q , JIA X , ZHU W G . Overview and key technology of cognitive electronic warfare based UAV group[J]. Modern Defence Technology, 2017, 45 (6): 173- 177.
36	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, 2019, 49 (9): 1420- 1430.
	ZHANG Y , SI G Y , WANG Y Z . Modeling of cooperative target allocation of the UAV swarmcyberspace attack action[J]. Systems Engineering and Electronics, 2019, 49 (9): 1420- 1430.
37	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- 8.
	YANG J Y , HU X F . New type operation evaluation based on SoS simulation testbed[J]. Operation Research and System Engineer, 2016, 30 (3): 5- 8.

指标名称	指标数值
指标名称	中小型固定翼	小型旋转翼
编组模式	有人机/无人机混合编组
单一集群规模/架	10~500	100~2 000
最大航程/km	600	300
滞空时间/h	>6	>10
飞行高度/m	50~5 000	10~3 000
起飞重量/kg	50~100	10~50
最大飞行速度/(m/s)	200	50
挂载装备	侦察、攻击一体化的智能电磁对抗装备

模型分辨率	建模仿真层次	作战行动层次	作战主体划分	典型样式
低	行动级	战役层	分群	无人蜂群雷达对抗行动
低	行动级	战役层	分群	无人蜂群通信对抗行动
中	任务级	战术层	子群	无人蜂群雷达侦察任务
				无人蜂群雷达攻击任务
				无人蜂群雷达防御任务
				无人蜂群通信侦察任务
				无人蜂群通信攻击任务
				无人蜂群通信防御任务
高	行为级	装备层	无人蜂群群体	无人蜂群电磁作战群体行为
高	行为级	装备层	无人蜂群个体	无人蜂群电磁作战个体行为

[1]	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.
[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]	周芳, 楚威, 丁冉. 情报驱动的平行仿真实体动态生成方法[J]. 系统工程与电子技术, 2018, 40(5): 1160-1166.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	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.