Research on the Target Allocation Method for Air Defense and Anti-missile Defense of Naval Ships

doi:10.16182/j.issn1004731x.joss.23-1219

Abstract

Abstract:

To solve the problems of multiple types of state information and correlation of time-series state information encountered in the dynamic weapon target assignment problem, a dynamic weapon target assignment method based on an improved deep reinforcement learning algorithm is proposed. A multi-input assignment model of target missile-interceptor unit, interceptor unit, and defense unit under multi-wave target and multi-phase is constructed. A multi-input state space is designed, and a Markov decision process is established in conjunction with the problem model. A feature extraction network combining multi-input information processing and gated recurrent network is designed, which improves the ability to extract state information, retains the necessary state information and forgets the unimportant state information, and the multi-head attention mechanism is introduced into the strategy network to improve the expressiveness and convergence speed of the model. As shown by the experimental results, the dynamic weapon target assignment method in this paper has better convergence speed and interception gain.

Key words: air defense and anti-missile, target assignment, weapons target assignment, DRL, attention mechanism, Advantage Actor-Critic

CLC Number:

TP391.9

Fei Shuaidi, Cai Changlong, Liu Fei, Chen Minghui, Liu Xiaoming. Research on the Target Allocation Method for Air Defense and Anti-missile Defense of Naval Ships[J]. Journal of System Simulation, 2025, 37(2): 508-516.

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References 20

1	李龙跃, 刘付显, 赵麟锋. 对多波次目标直接分配到弹的反导火力规划方法[J]. 系统工程与电子技术, 2014(11): 2206-2212.
	Li Longyue, Liu Fuxian, Zhao Linfeng. Direct Interceptor Allocation Method in Antimissile Firepower Planning for Multiple Wave Targets[J]. Systems Engineering and Electronics, 2014(11): 2206-2212.
2	姚继帅. 多导弹武器-目标分配关键技术研究[D]. 长沙: 国防科技大学, 2020.
	Yao Jishuai. Research on Key Technology of Multi-missile Weapon-target Assignment[D]. Changsha: National University of Defense Technology, 2020.
3	Matlin S. A Review of the Literature on the Missile-allocation Problem[J]. Operations Research, 1970, 18(2): 334-373.
4	Kline A, Ahner D, Hill R. The Weapon-target Assignment Problem[J]. Computers & Operations Research, 2019, 105: 226-236.
5	李梦杰, 常雪凝, 石建迈, 等. 武器目标分配问题研究进展: 模型、算法与应用[J]. 系统工程与电子技术, 2023, 45(4): 1049-1071.
	Li Mengjie, Chang Xuening, Shi Jianmai, et al. Developments of Weapon Target Assignment: Models, Algorithms, and Applications[J]. Systems Engineering and Electronics, 2023, 45(4): 1049-1071.
6	宋贵宝, 强裕功, 刘铁, 等. 动态武器目标分配问题的现状与进展[J]. 兵器装备工程学报, 2022, 43(12): 83-88.
	Song Guibao, Qiang Yugong, Liu Tie, et al. The Present Situation and Progress of Dynamic Weapon Target Assignment[J]. Journal of Ordnance Equipment Engineering, 2022, 43(12): 83-88.
7	葛志欣. 基于深度强化学习的多智能体协同决策研究[D]. 大连: 大连理工大学, 2021.
	Ge Zhixin. Research on Collaborative Decision of Multi-agent Based on Deep Reinforcement Learning[D]. Dalian: Dalian University of Technology, 2021.
8	张梦钰, 豆亚杰, 陈子夷, 等. 深度强化学习及其在军事领域中的应用综述[J]. 系统工程与电子技术, 2024, 46(4): 1297-1308.
	Zhang Mengyu, Dou Yajie, Chen Ziyi, et al. Review of Deep Reinforcement Learning and Its Applications in Military Field[J]. Systems Engineering and Electronics, 2024, 46(4): 1297-1308.
9	李冬雪. 基于深度强化学习的资源分配问题研究[D]. 北京: 北京交通大学, 2021.
	Li Dongxue. Research on Resource Assignment Problem Based on Deep Reinforcement Learning[D]. Beijing: Beijing Jiaotong University, 2021.
10	熊蓉玲, 段春怡, 冉华明, 等. 基于深度强化学习的智能决策方法[J]. 电讯技术, 2023, 63(1): 1-6.
	Xiong Rongling, Duan Chunyi, Ran Huaming, et al. An Intelligent Decision Making Method Based on Deep Reinforcement Learning[J]. Telecommunications Technology, 2023, 63(1): 1-6.
11	Li Xiaoyang, Zhou Deyun, Yang Zhen, et al. A Novel Genetic Algorithm for the Synthetical Sensor-weapon-target Assignment Problem[J]. Applied Sciences, 2019, 9(18): 3803.
12	Chang Tianqing, Kong Depeng, Hao Na, et al. Solving the Dynamic Weapon Target Assignment Problem by an Improved Artificial Bee Colony Algorithm with Heuristic Factor Initialization[J]. Applied Soft Computing, 2018, 70: 845-863.
13	Chang Yizhe, Li Zhanwu, Kou Yingxin, et al. A New Approach to Weapon-target Assignment in Cooperative Air Combat[J]. Mathematical Problems in Engineering, 2017, 2017(1): 2936279.
14	龙腾, 刘震宇, 史人赫, 等. 基于神经网络的防空武器目标智能分配方法[J]. 空天防御, 2021, 4(1): 1-7.
	Long Teng, Liu Zhenyu, Shi Renhe, et al. Neural Network Based Air Defense Weapon Target Intelligent Assignment Method[J]. Air & Space Defense, 2021, 4(1): 1-7.
15	肖友刚, 金升成, 毛晓, 等. 基于深度强化学习的舰船导弹目标分配方法[J/OL]. 控制理论与应用. (2023-06-12) [2023-09-24]. .
	Xiao Yougang, Jin Shengcheng, Mao Xiao, et al. Deep Reinforcement Learning Based Target Assignment Method for Ship Missile[J/OL]. Control Theory & Applications. (2023-06-12) [2023-09-24]. .
16	李思远. 面向智能军事决策的深度强化学习算法研究[D]. 太原: 中北大学, 2022.
	Li Siyuan. Research on Deep Reinforcement Learning Algorithm for Intelligent Military Decision[D]. Taiyuan: North University of China, 2022.
17	朱建文, 赵长见, 李小平, 等. 基于强化学习的集群多目标分配与智能决策方法[J]. 兵工学报, 2021, 42(9): 2040-2048.
	Zhu Jianwen, Zhao Changjian, Li Xiaoping, et al. Multi-target Assignment and Intelligent Decision Based on Reinforcement Learning[J]. Acta Armamentarii, 2021, 42(9): 2040-2048.
18	Junyoung Chung, Gulcehre Caglar, Cho KyungHyun, et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[EB/OL]. (2014-12-11) [2023-10-08]. .
19	Lipton Z C, Berkowitz J, Elkan C. A Critical Review of Recurrent Neural Networks for Sequence Learning[EB/OL]. (2015-10-17) [2023-10-08]. .
20	Vaswani A, Shazeer N, Parmar N, et al. Attention Is All You Need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017: 6000-6010.

属性	I型弹道导弹弹头	II型弹道导弹弹头
毁伤范围/m	1 000	500
马赫数	4	3.2
价值	6	4
威胁参数	0.8	0.5

属性	I型拦截单元	II型拦截单元
拦截范围/km	10~400	5~300
剩余拦截弹	8	8
拦截弹价值	4	2
拦截率/%	85	80
对I型导弹拦截率/%	85	60
对II型导弹拦截率/%	65	90
速度/(Ma/s)	2.8	2.3

属性	护卫舰	航母
重要程度	1	3
价值	15	30

参数	设置
步长	1 000
总步长	2×10⁵
输入维度	128
输出维度	128
隐藏层维度	128
学习率	3×10^-3
超参数	1×10^-2
熵系数	0.01
经验数量	100

训练次数	PPO	A2C	CAMDRL	A2C_G算法
10	2 min 40 s	2 min 32 s	2 min 40 s	2 min 42 s
30	2 min 31 s	2 min 28 s	2 min 27 s	2 min 25 s
50	2 min 19 s	2 min 20 s	2 min 18 s	2 min 16 s