A Method for UAV Cooperative Jamming Task Allocation based on HPSO Algorithm

doi:10.12382/bgxb.2025.0142

Abstract

Abstract:

For the collaborative optimization problem of multiple unmanned aerial vehicles (UAVs) jamming the ground communication networks，this paper aims to minimize the maximum power of multiple jamming UAVs under the constraint of suppressing the entire communication network.The complex interference resource allocation problem is transformed into a traditional combinatorial optimization problem with constraints.A hybrid particle swarm optimization (HPSO) algorithm based on particle swarm optimization(PSO) is designed，which simultaneously optimizes the three-dimensional positions of UAVs and the jammer power by treating them as particle positions，and incorporates a weapon threat model. HPSO algorithm，sparrow search algorithm and genetic algorithm arecompared through simulation experiment.The results show that HPSO algorithm has faster convergence speed and can efficiently find suitable UAV deployment solutions.In mission scenarios with different numbers of UAVs，HPSO algorithm demonstrates effective jamming performance and strong stability，making it a more suitable algorithm for optimizing the task of multi-UAV cooperatively jamming the ground communication networks.The research findings provide an effective solution and algorithm for multi-UAV cooperatively jamming the ground communication networks，which is of great significance for enhancing unmanned cyber-electronic warfare capabilities.

Key words: cooperative jamming, particle swarm optimization algorithm, unmanned aerial vehicle, ground communication network, network-electronic warfare

CHEN Fawei, CHEN Song, WANG Sheng, LIU Chencheng, YUE Jiaying. A Method for UAV Cooperative Jamming Task Allocation based on HPSO Algorithm[J]. Acta Armamentarii, 2025, 46(S1): 250142-.

Figures/Tables 12

References 25

[1]	成天桢, 余继周, 邢德奎. 网电空间攻击研究与对抗技术分析[J]. 现代防御技术, 2016, 44(3):91-98.
	CHENG T Z, YU J Z, XING D K. Attacks in cyberspace and corresponding countermeasures[J]. Modern Defense Technology, 2016, 44(3):91-98. (in Chinese)
[2]	张志威, 张传富, 岳云天. 网电空间中基于蠕虫的攻防对抗技术研究[J]. 计算机工程, 2013, 39(11):119-122. doi: 10.3969/j.issn.1000-3428.2013.11.026
	ZHANG Z W, ZHANG C F, YUE Y T. Research on attack-defense countermeasure technology based on Worm in cyberspace[J]. Computer Engineering, 2013, 39 (11):119-122. (in Chinese)
[3]	CHAO S, HUI T C, JIE G, et al. Multi-UAV interference coordination via joint trajectory and power control[J]. IEEE Transactions on Signal Processing, 2020,68843-858.
[4]	SUN Q K, LIU W F, CAI L. Multi-dynamic target coverage tracking control strategy based on multi-UAV collaboration[J]. Control Engineering Practice, 2025,155106170-106170.
[5]	WANG F, ZHANG Z Q, ZHOU L Y, et al. Robust multi-UAV cooperative trajectory planning and power control for reliable communication in the presence of uncertain jammers[J]. Drones, 2024, 8(10):558-558.
[6]	郑超. 无人机对地域通信网侦察干扰任务规划研究[D]. 长沙: 国防科技大学, 2017.
	ZHENG C. Research on mission planning of reconnoitering and jamming field communication Network by UAV[D]. Changsha: National University of Defense Technology, 2017. (in Chinese)
[7]	WANG X Y, DEI Z S, HUANG J X, et al. Joint resource allocation and power control for radar interference mitigation in multi-UAV networks[J]. Science China Information Sciences, 2021, 64(8):
[8]	GAO Z, SUN H, LI F, et al. Cooperative jamming of multi-UAVs to netted radar[J]. Journal of Physics:Conference Series, 2021, 1865(2):
[9]	赵士杰, 李鹏飞, 赖莉. 全域空间无人机集群协同干扰模型[J]. 四川大学学报(自然科学版), 2022, 59(5):30-38.
	ZHAO S J, LI P F, LAI L. A cooperative jamming model of UAV cluster in global space[J]. Journal of Sichuan University (Natural Science Edition), 2022, 59 (5):30-38. (in Chinese)
[10]	ZHANG W, MA D, ZHAO Z, et al. Design of cognitive jamming decision-making system against MFR based on reinforcement learning[J]. IEEE Transactions on Vehicular Technology. Florence, 2023:1-15.
[11]	ZHANG W X, ZHAO T, ZHAO Z K, et al. Performance analysis of deep reinforcement learning-based intelligent cooperative jamming method confronting multi-functional networked radar[J]. Signal Processing, 2023,207
[12]	邹玮琦. 多无人机协同干扰雷达的智能控制技术研究[D]. 郑州: 战略支援部队信息工程大学, 2023.
	ZOU W Q. Research on intelligent control technology of multi-UAV cooperative radar Jamming[D]. Zhengzhou: Information Engineering University of Strategic Support Force, 2023. (in Chinese)
[13]	刘旖菲, 李小帅, 杨俊安, 等. 基于SANER-PPO算法的无人机集群干扰资源分配方法[J]. 控制与决策, 2024, 39(12):3937-3945.
	LIU Y F, LI X S, YANG J A, et al. SANER-PPO algorithm-based jamming resource allocation for UAV swarm[J]. Control and Decision, 2024, 39(12):3937-3945. (in Chinese)
[14]	黄郡, 单洪, 满毅. 基于区域覆盖的协同干扰任务分配模型及算法[J]. 兵工学报, 2011, 32(6):725-732.
	HUANG J, SHAN H, MAN Y. Coordinated jamming task assignment model and algorithm based on region covering[J]. Acta Armamentarii, 2011, 32(6):725-732. (in Chinese)
[15]	邓杨赟, 李文光, 葛佳昊, 等. 多无人机协同搜索及干扰算法研究[J]. 战术导弹技术, 2023,(5):10-18,113.
	DENG Y Y, LI W G, GE J H, et al. Research on cooperative search and jamming algorithm of multiple UAVs[J]. Tactical Missile Technology, 2023(5):10-18,113. (in Chinese)
[16]	张澳, 杨渡佳, 王健, 等. 基于注意力预训练自编码器的无人机集群干扰资源分配方法[J]. 控制与决策, 2025, 40(5):1571-1580.
	ZHANG A, YANG D J, WANG J, et al. UAV swarm jamming resource allocation method based on attention pretrained self-encoder[J]. Control and Decision, 2025, 40(5):1571-1580. (in Chinese)
[17]	郑超, 许阳明. 多无人机在协同压制无线通信网链路中的应用[J]. 兵器装备工程学报, 2017, 38(11):129-133,137.
	ZHENG C, XU Y M. Application in multi-UAV cooperatively suppressing the wireless links of communication network[J]. Journal of Ordnance Equipment Engineering, 2017, 38(11):129-133137. (in Chinese)
[18]	WANG J F, JIA G W, LIN J C, et al. Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm[J]. Journal of Central South University:Science & Technology of Mining and Metallurgy, 2020, 27(2):432-448.
[19]	XU S F, LI L L, ZHOU Z Y, et al. A task allocation strategy of the UAV swarm based on multi-discrete wolf pack algorithm[J]. Applied Sciences, 2022, 12(3):1331-1331.
[20]	LU Y T, MA Y F, WANG J Y, et al. Task Assignment of UAV swarm based on wolf pack algorithm[J]. Applied Science-BASEL, 2020, 10(23):8335-.
[21]	邓敏, 伍志高, 姚志强, 等. 带精英集并行遗传算法的无人机干扰资源调度[J]. 电子与信息学报, 2022, 44(6):2158-2165.
	DENG M, WU Z G, YAO Z Q, et al. Unmanned aerial vehicle jamming resource scheduling based on parallel genetic algorithm with Elite set[J]. Journal of Electronics & Information Technology, 2022, 44(6):2158-2165. (in Chinese)
[22]	闫少强, 杨萍, 刘卫东, 等. 基于GPSSA算法的复杂地形多无人机航迹规划[J]. 北京航空航天大学学报, 2025, 51(1):303-313.
	YAN S Q, YANG P, LIU W D, et al. Multi-UAV trajectory planning for complex terrain based on GPSSA algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2025, 51(1):303-313. (in Chinese)
[23]	徐东辉, 傅晓宁, 康凯. 典型地貌条件下无线宽带通信站点布设规划设计与实现[J]. 信息通信, 2020,(10):6-9.
	XU D H, FU X N, KANG K. Planning and implementation of wireless broadband communication site layout under typical geomorphological conditions[J]. Information & Communications, 2020(10):6-9. (in Chinese)
[24]	XU C, XU M, YIN C J. Optimized multi-UAV cooperative path planning under the complex confrontation environment[J]. Computer Communications, 2020, 162(prepublish):196-203.
[25]	KENNEDY J, EBERHART R.C. Particle swarm optimization[C]// Proceedings of IEEE International Conferences on Neural Networks. Perth,WA,Australia:IEEE, 1995.

参数		值	单位
通信端	工作频率	280	MHz
	发射功率	20	W
	发射天线增益	6	dbi
	接收天线增益	6	dbi
	发射天线高度	1.5	m
	接收天线高度	1.5	m
反无人机武器	有效攻击距离	1.6	km
	最小攻击半径	0.1	km
	攻击高度	1.6	km
干扰端	工作频率	280	GHz
	最大发射功率	10	W
	天线增益	3	dbi
	波束角度	90°	\
	信号合成效率	60%	\
	干扰门限	3	\

参数		值	单位
通信端	工作频率	280	MHz
	发射功率	20	W
	发射天线增益	6	dbi
	接收天线增益	6	dbi
	发射天线高度	1.5	m
	接收天线高度	1.5	m
反无人机武器	有效攻击距离	1.6	km
	最小攻击半径	0.1	km
	攻击高度	1.6	km
干扰端	工作频率	280	GHz
	最大发射功率	10	W
	天线增益	3	dbi
	波束角度	90°	\
	信号合成效率	60%	\
	干扰门限	3	\

算法	参数	值
通用	种群规模	100
	最大迭代次数	500
	通信节点数量	3/4/5
	无人机数量	1/2/3/4/5
HPSO	学习因子	c1=1.5 c2=2.0
HPSO	迭代权重下降率	0.99
PSO	学习因子	c1=1.5 c2=2.0
SSA	发现者比例	0.2
SSA	预警值	0.8
GA	突变概率	0.1
GA	交叉概率	0.7

算法	参数	值
通用	种群规模	100
	最大迭代次数	500
	通信节点数量	3/4/5
	无人机数量	1/2/3/4/5
HPSO	学习因子	c1=1.5 c2=2.0
HPSO	迭代权重下降率	0.99
PSO	学习因子	c1=1.5 c2=2.0
SSA	发现者比例	0.2
SSA	预警值	0.8
GA	突变概率	0.1
GA	交叉概率	0.7

优化算法	收敛结果 /mW	收敛所需迭代次数	500次迭代耗时/s
HPSO	198.2	349	3.4132
PSO	845.0	120	3.1365
GA	213.6	413	2.1105
SSA	568.9	397	2.7814