基于火炮转移路径预测的无人机集群反炮兵搜索路径规划

doi:10.12305/j.issn.1001-506X.2025.04.19

Abstract

Abstract:

Modern artillery has the characteristics of maneuver combat, quick-hitting and fast-retreating. It is difficult to hit it directly based on the position provided by the artillery reconnaissance radar. Therefore, a method for planning search paths for unmanned aerial vehicle (UAV) swarm to against artillery based on the prediction of the transfer path of artillery is proposed. The proposed method improves artillerg discover and strike effciency of UAV swarm by predicting the transfer path of artillery after shotting. The model of the battlefield environment and combat elements is established, and the concept of combat state and geomorphic environment suitability are proposed. The Markov state model of artillery combat is utilized to combine the combat state and geomorphic environment suitability for artillery transfer path prediction. An UAV swarm search path planning algorithm is constructed based on rolling time-domain optimization, and the search path optimization problem under sparse pheromone distribution is solved through the newly designed expected reward term in the objective function. The results of the simulation demonstrate that the proposed method is capable of effectively utilizing battlefield information to predict the possible transfer paths of blue artillery. In comparison to other methods for comparison, the proposed method demonstrates certain advantages in terms of effectiveness and stability in search and strike tasks, which provides a basis for subsequent practical applications.

Key words: anti-artillery combat, unmanned aerial vehicle (UAV) swarm, Markov state model, search path planning

CLC Number:

TP391

Ze GENG, Yanyan HUANG, Han ZHANG. UAV swarm anti-artillery search path planning based on artillery transfer path prediction[J]. Systems Engineering and Electronics, 2025, 47(4): 1222-1234.

Figures/Tables 28

Fig.1

Fig.2

Fig.3

Table 1

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 2

Table 3

Table 4

Fig.10

Fig.11

Fig.12

Fig.13

Table 5

Fig.14

Fig.15

Fig.16

Fig.17

Fig.18

Table 6

Fig.19

Fig.20

Table 7

Fig.21

References 31

1	MOHAMMAD S , HIMANSHU M , SHREYA P , et al. Routing and scheduling optimization for UAV assisted delivery system: a hybrid approach[J]. Applied Soft Computing, 2022, 126, 109225. doi: 10.1016/j.asoc.2022.109225
2	WANG G H , WANG F M , WANG J H , et al. Collaborative target assignment problem for large-scale UAV swarm based on two-stage greedy auction algorithm[J]. Aerospace Science and Technology, 2024, 149, 109146. doi: 10.1016/j.ast.2024.109146
3	LAGHARI A A , JUMANI A K , LAGHARI R A , et al. Unmanned aerial vehicles: a review[J]. Cognitive Robotics, 2023, 3, 8- 22. doi: 10.1016/j.cogr.2022.12.004
4	SHAO R , TAO R T , LIU Y D , et al. UAV cooperative search in dynamic environment based on hybrid-layered APF[J]. EURASIP Journal on Advances in Signal Processing, 2021, 2021 (1): 101. doi: 10.1186/s13634-021-00807-6
5	HU J Q , WU H S , ZHAN R J , et al. Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior[J]. Journal of Systems Engineering and Electro-nics, 2021, 32 (6): 1463- 1476. doi: 10.23919/JSEE.2021.000124
6	ZHANG C Q , ZHOU W J , QIN W D , et al. A novel UAV path planning approach: heuristic crossing search and rescue optimization algorithm[J]. Expert Systems with Applications, 2023, 215, 119243. doi: 10.1016/j.eswa.2022.119243
7	WANG Z Q , LI J , LI J , et al. A decentralized decision-making algorithm of UAV swarm with information fusion strategy[J]. Expert Systems with Applications, 2024, 237, 121444. doi: 10.1016/j.eswa.2023.121444
8	LI Y B , ZHANG Z T , SUN Q , et al. A distributed framework for multiple UAV cooperative target search under dynamic environment[J]. Journal of the Franklin Institute, 2024, 361 (8): 106810. doi: 10.1016/j.jfranklin.2024.106810
9	ALFEO A L , CIMINO M G C A , DE F N , et al. Design and simulation of the emergent behavior of small drones swarming for distributed target localization[J]. Journal of Computational Science, 2018, 29, 19- 33. doi: 10.1016/j.jocs.2018.09.014
10	SHAO Y , ZHAO Z F , LI R P , et al. Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21 (5): 796- 808.
11	RYAN D , JOHN S . A framework for multi-UAV persistent search and retrieval with stochastic target appearance in a continuous space[J]. Journal of Intelligent & Robotic Systems, 2021, 103 (4): 65.
12	DANILO S , LEONARDO C , LORENZO F . Multitrajectory model predictive control for safe UAV navigation in an unknown environment[J]. IEEE Trans.on Control Systems Technology, 2023, 31 (5): 1982- 1997. doi: 10.1109/TCST.2022.3216989
13	YAO P , WEI X . Multi-UAV information fusion and cooperative trajectory optimization in target search[J]. IEEE Systems Journal, 2022, 16 (3): 4325- 4333. doi: 10.1109/JSYST.2021.3117959
14	LI Y B, ZHANG S T, CHEN J, et al. Multi-UAV cooperative mission assignment algorithm based on ACO method[C]//Proc. of the IEEE International Conference on Computing, Networking and Communications, 2020: 304-308.
15	文超, 董文瀚, 解武杰, 等. 基于回访机制的无人机集群分布式协同区域搜索方法[J]. 航空学报, 2023, 44 (11): 253- 270.
	WEN C , DONG W H , XIE W J , et al. Distributed cooperative area search method for UAV swarms based on revisit mechanism[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (11): 253- 270.
16	XU C , XU M , YIN C J . Optimized multi-UAV cooperative path planning under the complex confrontation environment[J]. Computer Communications, 2020, 162, 196- 203. doi: 10.1016/j.comcom.2020.04.050
17	TAREK M, ABOSREA M, ELNAGAR A R, et al. Design and implementation of a cymbeline radar signal processor with waveform generator to detect small RCS artillery bombs on a single FPGA chip[C]//Proc. of the International Undergraduate Research Conference, 2021, 5(5): 277-280.
18	SWIETOCHOWSKI N . Rules of artillery employment in combat operations[J]. Scientific Journal of the Military University of Land Forces, 2019, 51 (2): 280- 293.
19	陈雅茜, 高亦远, 葛荣存, 等. 一种提高栅格成本距离分析准确性的改进算法[J]. 地理与地理信息科学, 2020, 36 (3): 18- 24.
	CHEN Y X , GAO Y Y , GE R C , et al. An improved algorithm for increasing the accuracy of raster cost distance analysis[J]. Geography and Geo-Information Science, 2020, 36 (3): 18- 24.
20	JING X R, YANG X J. Application and improvement of heuristic function in A^* algorithm[C]//Proc. of the IEEE 37th Chinese Control Conference, 2018: 2191-2194.
21	ZHANG P Q, HUA Y H, LI T. Dynamic trajectory planning and tracking algorithm of lunar rover with updating map information[C]//Proc. of the IEEE China Automation Congress, 2022: 4024-4029.
22	WANG H W , QI X Y , LOU S J , et al. An efficient and robust improved A^* algorithm for path planning[J]. Symmetry, 2021, 13 (11): 2213.
23	孟光磊, 李树发, 刘彬斌, 等. 防空预警雷达高原环境适应性评估的自学习模糊灰度方法[J]. 兵工学报, 2022, 43 (1): 98- 110.
	MENG G L , LI S F , LIU B B , et al. Self-learning fuzzy grey method for plateau environmental adaptability assessment of air defense early-warning radar[J]. Acta Armamentarii, 2022, 43 (1): 98- 110.
24	SHAO Y , ZHAO Z F , LI R P , et al. Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21 (5): 796- 808.
25	FEI B W , BAO W D , ZHU X M , et al. Autonomous cooperative search model for multi-UAV with limited communication network[J]. IEEE Internet of Things Journal, 2022, 9 (19): 19346- 19361.
26	谢黎焱. 基于马尔柯夫过程的自行炮兵机动作战研究[J]. 指挥控制与仿真, 2010, 32 (4): 16-20, 27.
	XIE L Y . Research on maneuvering combat of self-propelled cannon based on Markov[J]. Command Control & Simulation, 2010, 32 (4): 16-20, 27.
27	方兆本, 缪柏其. 随机过程[M]. 北京: 科学出版社, 2011.
	FANG Z B , MIAO B Q . Stochastic process[M]. Beijing: China Science Publishing, 2011.
28	SCHWENZER M , AY M , BERGS T , et al. Review on model predictive control: an engineering perspective[J]. The International Journal of Advanced Manufacturing Technology, 2021, 117, 1327- 1349.
29	ALANEZI M A , BOUCHEKARA H R E H , APALARA T A , et al. Dynamic target search using multi-UAVs based on motion-encoded genetic algorithm with multiple parents[J]. IEEE Access, 2022, 10, 77922- 77939.
30	LI B , SONG C , BAI S X , et al. Multi-UAV trajectory planning during cooperative tracking based on a fusion algorithm integrating MPC and standoff[J]. Drones, 2023, 7 (3): 196.
31	WANG Z J , ZHAN Z H , KWONG S , et al. Adaptive granula-rity learning distributed particle swarm optimization for large-scale optimization[J]. IEEE Trans.on Cybernetics, 2020, 51 (3): 1175- 1188.

适宜程度	适宜度f取值	描述
优	4	该项作战活动在该地貌环境下进行时取得最大效能
良	3	该项作战活动能够顺利在该地貌环境下进行, 效能良好
中等	2	该项作战活动能够在该地貌环境下进行, 效能一般
较差	1	该项作战活动在该地貌环境下进行时存在困难, 效能低下
差	0	该项作战活动无法在该地貌环境下进行

参数	取值	参数	取值
a	200	δ	0.5
b	160	T_r/Δt	1
l/m	50	v^u/(l·Δt^-1)	4
Δt/s	30	θ_max/(°)	45
α_c	1.33	r/l	1.5
$\epsilon$	0.8	-	-

参数	取值	参数	取值
输入维度	5	w	0.8
种群数量	20	c₁	0.5
最大迭代次数	200	c₂	0.5

参数	取值	参数	取值
α₁	3	ω₃	1
β₁	8.7	d_set/l	15
α₂	2.7	T_p/Δt	1
β₂	2.2	T_f/Δt	5
p₁₂	1	σ₁	1.8
p₂₁	1	σ₂	1
d	14	σ₃	-30
ω₁	1.5	d_p	5
ω₂	10	-	-

编号	位置坐标	航向/(°)
自行火炮1	(44, 124)	-
自行火炮2	(31, 34)	-
自行火炮3	(194, 130)	-
自行火炮4	(129, 48)	-
无人机1	(40, 160)	-90
无人机2	(80, 160)	-90
无人机3	(120, 160)	-90
无人机4	(160, 160)	-90

UAV swarm anti-artillery search path planning based on artillery transfer path prediction

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 28

References 31

Related Articles 13

Recommended Articles

Metrics

Comments

实验结果	对照组	实验组
仿真时间步均值	63.90	68.05
仿真时间步标准差	14.87	16.91
累计射击次数均值	36.85	38.95
累计射击次数标准差	5.90	7.85

实验结果	集群中无人机数量
实验结果	4	6	8	10
仿真时间步均值	103.05	60.75	55.20	54.20
仿真时间步标准差	26.11	14.70	10.32	10.38
累计射击次数均值	52.60	34.30	33.20	31.95
累计射击次数标准差	11.85	6.25	3.60	2.94

[1]	Wenhao BI, Mengqi ZHANG, Fei GAO, Mi YANG, An ZHANG. Review on UAV swarm task allocation technology [J]. Systems Engineering and Electronics, 2024, 46(3): 922-934.
[2]	Tao LIU, Shasha WANG, Chi ZHANG, Guanghan BAI, Junyong TAO. Resilience based self-organizing region coverage method for unmanned aerial vehicle swarm [J]. Systems Engineering and Electronics, 2024, 46(3): 942-952.
[3]	Cheng GAO, Yanli DU, Yunong BU, Yanbin LIU, Yufei WANG. Heterogeneous UAV swarm grouping deployment for complex multiple tasks [J]. Systems Engineering and Electronics, 2024, 46(3): 972-981.
[4]	Yaxuan YIN, An ZHANG, Wenhao BI, Pan YANG, Zhanjun HUANG. Flocking control for quadrotor unmanned aerial vehicle swarm with dynamic topology [J]. Systems Engineering and Electronics, 2024, 46(10): 3473-3483.
[5]	Xiaogang QI, Yutong ZHOU, Lifang LIU. Evaluation of the reliability of UAV swarm for ground combat missions [J]. Systems Engineering and Electronics, 2023, 45(9): 2971-2978.
[6]	Shuheng ZHANG, Ruping ZHAI, Yongkai LIU. Identification of UAV swarm type based on fusion features of communication and radar domain [J]. Systems Engineering and Electronics, 2023, 45(12): 3734-3742.
[7]	Ruping ZHAI, Shuheng ZHANG, Jiarong PING. Waveform recognition of unmanned aerial vehicle swarm communication in complex multipath environment [J]. Systems Engineering and Electronics, 2023, 45(10): 3312-3320.
[8]	Xuping GU, Daquan TANG. Hierarchical cooperative navigation of UAV swarm based on federated filtering algorithm [J]. Systems Engineering and Electronics, 2022, 44(3): 967-976.
[9]	Xingjia YANG, Keqing DUAN, Xiang LI, Wei QI. Resolving range ambiguity for cooperative detection using UAV swarms [J]. Systems Engineering and Electronics, 2022, 44(2): 480-489.
[10]	Xiaowei FU, Jing PAN. Distributed formation control of UAV swarm with dynamic obstacle avoidance [J]. Systems Engineering and Electronics, 2022, 44(2): 529-537.
[11]	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.
[12]	ZHANG Yang, SI Guangya, WANG Yanzheng. Modeling of cooperative target allocation of the UAV swarm cyberspace attack action [J]. Systems Engineering and Electronics, 2019, 41(9): 2025-2033.
[13]	GAO Yang, LI Dongsheng. Swarm situation perception consensus evaluation via intervalnumber Heronian operators with variable weights [J]. Systems Engineering and Electronics, 2019, 41(1): 89-95.