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非完全信息下无人机集群对抗研究综述

薛健 赵琳 向贤财 吕科 宏晨 张宝琳 岩延 王泳

薛健, 赵琳, 向贤财, 吕科, 宏晨, 张宝琳, 岩延, 王泳. 非完全信息下无人机集群对抗研究综述[J]. 电子与信息学报, 2024, 46(4): 1157-1172. doi: 10.11999/JEIT230544
引用本文: 薛健, 赵琳, 向贤财, 吕科, 宏晨, 张宝琳, 岩延, 王泳. 非完全信息下无人机集群对抗研究综述[J]. 电子与信息学报, 2024, 46(4): 1157-1172. doi: 10.11999/JEIT230544
XUE Jian, ZHAO Lin, XIANG Xiancai, LÜ Ke, HONG Chen, ZHANG Baolin, YAN Yan, WANG Yong. A Review of the Research on UAV Swarm Confrontation under Incomplete Information[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1157-1172. doi: 10.11999/JEIT230544
Citation: XUE Jian, ZHAO Lin, XIANG Xiancai, LÜ Ke, HONG Chen, ZHANG Baolin, YAN Yan, WANG Yong. A Review of the Research on UAV Swarm Confrontation under Incomplete Information[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1157-1172. doi: 10.11999/JEIT230544

非完全信息下无人机集群对抗研究综述

doi: 10.11999/JEIT230544
基金项目: 国家重点研发计划(2018AAA0100804)
详细信息
    作者简介:

    薛健:男,教授,研究方向为多智能体系统、模式识别和机器视觉

    赵琳:女,博士生,研究方向为深度强化学习、无人机集群系统、博弈论

    向贤财:男,硕士生,研究方向为深度强化学习、无人机集群系统

    吕科:男,教授,研究方向为人工智能、计算机视觉

    宏晨:男,副教授,研究方向为深度强化学习、无人机集群控制

    张宝琳:男,教授,研究方向为无人机集群控制

    岩延:男,副教授,研究方向为深度强化学习

    王泳:男,讲师,研究方向为复杂系统建模与优化、模式识别、数据挖掘

    通讯作者:

    王泳 wangyong@ucas.ac.cn

  • 中图分类号: TN975; TP391; TP181; V279; V249

A Review of the Research on UAV Swarm Confrontation under Incomplete Information

Funds: The National Key Research and Development Program of China (2018AAA0100804)
  • 摘要: 无人机集群以其具备的应用优势及发展前景,成为当前人工智能领域研究者关注的热点之一。而非完全信息下的无人机集群对抗技术,因其集群结构变化的高动态性以及环境信息复杂多变且不能完全感知的特点,成为对集群协同性与智能性要求最高的研究方向之一。其研究成果可以促进智能化无人系统的快速发展和广泛应用。该文全面回顾了非完全信息环境下无人机集群对抗研究的最新进展,按照包以德循环理论的思路将无人机集群对抗过程划分为态势评估、意图推断、任务规划与机动决策4个相互衔接的关键组成部分,并进一步将其细分为8个子研究目标。通过分析比较近年来的相关研究,着重阐述了无人机集群对抗领域各项任务的研究重点和难点以及已取得的成果,并讨论了无人机集群对抗技术所面临的挑战,包括大规模异构集群的协同控制、非完全信息的处理、复杂决策过程的建模以及实际应用任务的应对等。
  • 图  1  无人机集群对抗研究脉络

    图  2  无人机集群对抗态势评估研究框架

    图  3  无人机集群对抗中的意图推断研究框架

    图  4  无人机集群对抗机动决策研究框架

    表  1  非完全信息下无人机集群对抗具体研究内容

    OODA循环 对抗循环环节 具体内容 具体含义
    观察 态势评估 对抗态势评估 将态势定性分成优势、劣势、均势等,通过构造优势函数,利用双方状态信息进行态势评估,
    辅助集群决策
    威胁因素评估 根据无人机特点,评估排序敌方目标和防空威胁,以辅助目标分配和决策
    定位 意图推断 行为预测 利用已有信息推测敌机未来行为模式,预判使无人机集群获得决策优势
    意图识别 对敌方集群当前行为意图进行定性判断,理解敌机意图使无人机集群获得决策优势
    决策 任务规划 目标分配 考虑无人机的特性和任务需求,根据资源与优先级等约束,合理地分配目标,
    并实现集群内动态协同
    航迹规划 根据任务需求,为无人机规划最佳路径,以安全到达目标
    执行 机动决策 协同对抗 充分发挥集群力量,通过分散瓦解敌方防御,增大对敌方造成有效打击的可能性
    追踪合围 对敌方的动态追踪与合围,通过限制敌方行动来保持我方优势与对敌方的压制
    下载: 导出CSV

    表  2  无人机集群空战对抗态势评估方法研究对比

    参考文献 方法 仿真维度 无人机数量 影响因子权重 未来状态影响 非完全信息 异构集群
    Wu等人[6] 模糊推理 3D 1 vs. 1 客观计算 ×
    Zhang等人[7] 模糊神经网络 3D ${n} $ vs. ${n} $ 客观计算 × ×
    Huang等人[8] 贝叶斯推理 3D 1 vs. 1 客观计算
    Xie等人[9] 动态关联权值 3D 1 vs. 1 客观计算 ×
    高杨等人[10] 改进态势模型 ${n} $ vs. ${n} $ × ×
    Shin等人[12] 几何评分 3D 2 vs. 2 客观计算 × × ×
    √:考虑;×:不考虑;–:不涉及
    下载: 导出CSV

    表  3  无人机集群空战对抗中对敌方行为预测方法研究对比

    参考文献 方法 仿真维度 非完全信息 目标的复杂运动 计算难度的降低 目标数量 研究目标
    Pan等人[26] 状态预测影响图 2D × × 多个 机动决策
    Xi 等人[27] PSR-RBF 3D × × × 单个 空战情况感知和威胁评估
    Liu等人[28] GA-MASAC-TP Net 2D × × 多个 预测目标情况
    Tan等人[29] Bi-LSTM 2D × × × 单个 机动决策
    Yang等人[30] MPC 3D × × 单个 机动决策
    √: 考虑;×: 不考虑;–:不涉及
    下载: 导出CSV

    表  4  无人机集群目标分配方法研究对比

    参考文献 方法 仿真维度 异构集群 非完全信息 目标威胁 资源约束 时间约束
    严飞等人[40] CPN 2D × ×
    Zhen等人[41] 改进CPN 2D ×
    王峰等人[42] KnCMPSO 2D × ×
    Zhao等人[43] FTA × ×
    Jia等人[44] 改进GA-随机规划 2D ×
    赵玉亮等人[45] 多策略融合粒子群 × ×
    张安等人[46] IMSGWO 2D ×
    Liu等人[47] MARL × ×
    √:考虑;×:不考虑;–:不涉及
    下载: 导出CSV

    表  5  无人机集群航迹规划方法研究对比

    参考文献 方法 空间维度 异构集群 非完全信息 目标数量 环境威胁 防碰撞约束 时间协同约束
    蔡星娟等人[57] NSGAIII-ICO 3D × × 单目标
    左燕等人[58] GCRLB 2D × × 单目标 × ×
    Li等人[59] MN-DDPG 2D × 单目标 × ×
    王祝等人[60] SCP 3D × × 多目标
    严飞等人[40] 协同PSO 2D × 多目标 × ×
    Zhang等人[61] 模糊蚁群 2D × × 多目标 × × ×
    √:考虑;×:不考虑
    下载: 导出CSV

    表  6  无人机集群协同对抗方法研究对比

    参考文献 方法 空间维度 异构集群 非完全信息 目标分配 协同态势评估 意图推断 协同优势
    Ren等人[63] MADDPG 3D × ×
    Zhang等人[64] Actor-Critic 3D × × ×
    Li等人[65] Actor-Critic 3D × × × ×
    Wang等人[66] PD-MADDPG 2D × × × ×
    Yu等人[67] CLPIO 3D × × × ×
    Deng等人[68] 分布式任务分配 2D ×
    √:考虑;×:不考虑;≈:采用了与协同态势评估类似的方法
    下载: 导出CSV

    表  7  无人机集群追踪决策方法研究对比

    参考文献 方法 仿真维度 异构集群 目标数量 目标不确定运动 避障研究 复杂环境
    Memon等人[69] sLM-IPDA 3D × 多目标 ×
    Cybulski等人[70] 多重有向图 2D × 多目标 ×
    Yu等人[71] 自适应差分进化-Nash 3D × 单目标 ×
    Zhou等人[72] 最大互惠奖励MARL 2D × 多目标 ×
    Zhou等人[73] D3QN 2D × 多目标 × × ×
    Hua等人[74] 策略梯度 - 注意力 2D × 单目标 ×
    √: 考虑;×: 不考虑
    下载: 导出CSV

    表  8  无人机集群合围决策相关研究对比

    参考文献 方法 仿真维度 异构集群 复杂环境适应性 有限感知 目标数量 目标状态
    张岱峰等人[75] 狼群交互动力学模型 3D 多目标 移动
    Ma等人[76] DO-NS 2D × × × 单目标 移动
    Murat Ozbek等人[77] 改进DDPG 2D × × × 单目标 移动
    Li等人[78] 改进DDPG 3D × × 单目标 移动
    过劲劲等人[79] 一致性协议 2D × × 多目标 移动
    √: 考虑;×: 不考虑
    下载: 导出CSV
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  • 收稿日期:  2023-06-02
  • 修回日期:  2023-09-28
  • 网络出版日期:  2023-10-16
  • 刊出日期:  2024-04-24

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