面向自杀式无人机饱和攻击的海空跨域无人协同反制策略

梁霄; 陈聪; 刘殿勇; 于长东; 李巍

doi:10.11993/j.issn.2096-3920.2024-0007

面向自杀式无人机饱和攻击的海空跨域无人协同反制策略

doi: 10.11993/j.issn.2096-3920.2024-0007

1.
大连海事大学船舶与海洋工程学院, 辽宁大连, 116026
2.
大连海事大学人工智能学院, 辽宁大连, 116026
3.
大连海事大学环境科学与工程学院, 辽宁大连, 116026

基金项目: 国家自然科学基金资助项目(52271302); 国防基础科研项目(JCKY2022410C012); 辽宁省应用基础研究项目(2023JH2/101300198).

详细信息

作者简介:
梁霄：梁　霄(1980-), 男, 教授, 博导, 主要研究方向为海上无人系统决策与控制技术

中图分类号: V279; TP18
计量
- 文章访问数: 8
- HTML全文浏览量: 5
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2024-01-17
- 修回日期: 2024-03-08
- 录用日期: 2024-03-12
- 网络出版日期: 2024-03-25

Cooperative Countermeasure Strategy of Sea-Air Cross-Domain Unmanned Platforms For Saturation Attack of Suicide UAVs

1.
Naval Architecture and Ocean Engineering College, DaLian Maritime University, Dalian 116026, China
2.
College of Artificial Intelligence, Dalian Maritime University, Dalian 116026, China
3.
College of Environmental Sciences and Engineering, Dalian Maritime University, Dalian 116026, China

摘要

摘要: 针对海洋环境下反制自杀式无人机饱和攻击问题, 文中研究了目标数量远超我方情况下的海空跨域无人平台协同反制策略, 提出一种结合改进的遗传算法与联盟形成博弈的协同算法。首先根据海空跨域无人平台攻击特性与运动特性, 结合最大最小策略设计代价函数; 然后结合任务需求对遗传算法进行改进, 对交叉和变异过程进行引导和限制, 在提升遗传算法效率的基础上生成可行的反制方案; 最后设计联盟形成规则, 通过联盟之间的成员变动使各联盟达到纳什稳定状态, 在算子数量较多的情况下仍能使反制方案被持续稳定优化。仿真对比实验表明所提策略具有可行性和优越性, 能在目标遭受饱和攻击时提供合理高效的反制方案, 可为大规模跨域无人集群作战研究提供参考。
- 海空跨域 /
- 自杀式无人机 /
- 目标分配 /
- 遗传算法 /
- 联盟形成博弈
Abstract: Aiming at the problem of anti-suicide unmanned aerial vehicle saturation attacks in the marine environment, this paper studies the cooperative countermeasure strategy of sea and air cross-domain unmanned platforms under the condition that the number of targets far exceeds ours, and proposes a cooperative algorithm combining improved genetic algorithm and coalition formation game. Firstly, according to the sea-air cross-domain unmanned platform's attack characteristics and motion characteristics, the cost function is designed by combining the maximum and minimum strategies. Then, the genetic algorithm is improved according to the task requirements, the crossover and mutation processes are guided and restricted, and a feasible countermeasure scheme is generated based on improving the efficiency of the genetic algorithm. Finally, the coalition formation rules are designed, and the coalitions reach Nash stability through changing members between the coalitions. The countermeasure scheme can still be continuously and stably optimized for many operators. Simulation comparison experiments show that the proposed strategy is feasible and superior and can provide a reasonable and efficient countermeasure scheme when the target is subjected to a saturation attack. This can provide a reference for large-scale cross-domain unmanned swarm combat research.
- sea and air cross-domain /
- suicide unmanned aerial vehicle /
- target allocation /
- genetic algorithm /
- coalition formation game

HTML全文

图 1 双点交叉

Figure 1. Two-point crossing

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图 2 联盟形成博弈

Figure 2. Diagram of coalition formation games

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图 3 结合改进遗传算法与联盟形成博弈的协同反制方法流程

Figure 3. Flow chart of Collaborative countermeasures method combining Improved genetic algorithm and coalition formation games

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图 4 各场景反制方案

Figure 4. Countermeasures of each scene

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图 5 代价值变化曲线

Figure 5. Curves of value

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图 6 收敛时间对比图

Figure 6. Comparison of convergence time

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图 7 各场景推演过程图

Figure 7. Deduction process of each scene

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表 1 仿真环境设置

Table 1. Simulation environment setting

参数	数值
仿真区域	25 n mile×25 n mile
自杀式无人机机动性能	1(A型)、1.2(B型)、1.4(C型)、1.6(D型)
自杀式无人机防御能力	5(A型)、7(B型)、9(C型)、11(D型)
察打一体无人机机动性能	3(E型)、4(F型)
察打一体无人机攻击能力	2(E型)、4(F型)
大型无人艇载弹量	2(G型)、3(H型)

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