雷达探测群自主协同技术研究综述

胡佳; 丁建江; 周芬; 张阳; 周超群; 曲畅

doi:10.12000/JR26002

雷达探测群自主协同技术研究综述

DOI: 10.12000/JR26002 CSTR: 32380.14.JR26002

胡佳^{1, 2, ,},
丁建江¹,
周芬¹,
张阳^{1, 2},
周超群¹,
曲畅^{2, 3}

1.
空军预警学院武汉 430019
2.
信息支援部队工程大学武汉 430030
3.
国防科技大学研究生学院长沙 410073

基金项目: 国家重点研发计划(2022YFC3005700)，乾元国家实验室基金(KYZZ-F-02-202405-0005)

详细信息

作者简介:
胡　佳，助理研究员，主要研究方向为雷达协同探测、预警装备论证等

丁建江，教授，主要研究方向为雷达系统发展论证、试验评估、雷达协同运用等

周　芬，副教授，主要研究方向为预警装备论证、雷达组网运用、试验评估等

张　阳，助理研究员，主要研究方向为雷达资源管控、预警装备协同运用等

周超群，博士，主要研究方向为雷达资源管控、雷达组网运用等

曲　畅，高级工程师，主要研究方向为雷达组网运用、预警装备论证等

通讯作者:
胡佳 15629016325@163.com

责任主编：严俊坤 Corresponding Editor: YAN Junkun

中图分类号: TN95
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出版历程
- 收稿日期: 2026-01-04

A Survey on Autonomous Coordination Technology for Radar Networks

HU Jia^{1, 2
, ,},
DING Jianjiang¹,
ZHOU Fen¹,
ZHANG Yang^{1, 2},
ZHOU Chaoqun¹,
QU Chang^{2, 3}

1.
Air Force Early Warning Academy, Wuhan 430019, China
2.
Information Support Force Engineering University, Wuhan 430030, China
3.
Graduate School of National University of Defense Technology, Changsha 410073, China

Funds: National Key Research and Development Program of China (2022YFC3005700), Qianyuan Laboratory Foundation (KYZZ-F-02-202405-0005)

More Information

Corresponding author: HU Jia, 15629016325@163.com

摘要

摘要: 在复杂电磁环境与多目标协同探测需求的驱动下，通过自主协同技术提升雷达探测群的综合效能，已成为雷达协同探测领域的重要研究方向。国内外围绕该方向开展了广泛研究，在理论创新、技术验证与装备应用等方面取得了丰硕成果。该文首先系统梳理了雷达探测群自主协同的概念内涵与核心特征，在此基础上深入剖析了其在工程化实现与效能优化过程中面临的关键技术瓶颈；随后，围绕体系架构设计、协同感知、智能协同决策控制及自主协同演化4个维度，对近年来的代表性研究成果与技术路径进行了综述；最后，对该领域未来发展趋势进行了展望，以期为相关理论研究与工程实践提供参考。
- 探测群自主协同 /
- 体系架构 /
- 协同感知 /
- 智能协同决策控制 /
- 自主协同演化
Abstract: Driven by complex electromagnetic environments and multi-target collaborative detection needs, enhancing the overall effectiveness of radar networks through autonomous coordination technology has become a key research area in radar collaborative surveillance. Extensive research has been conducted worldwide, yielding substantial advances in theoretical development, technical validation, and equipment application. This paper systematically discusses the foundational concepts and main features of autonomous coordination in radar networks, examining the primary technical challenges faced during implementation and performance optimization. It also reviews recent notable research findings and technological strategies, focusing on collaborative architecture design, sensing, intelligent decision-making and control, and autonomous evolution. Finally, this paper offers an outlook on future trends in the field and provides references for related theoretical research and practical applications.
- Autonomous coordination of radar networks /
- System architecture /
- Collaborative sensing /
- Intelligent collaborative decision-making and control /
- Autonomous collaborative evolution

HTML全文

图 1 雷达探测群自主协同技术挑战

Figure 1. Technical challenges in autonomous collaborative surveillance of radar networks

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图 2 雷达探测群自主协同闭环模型

Figure 2. Closed-Loop model for autonomous collaborative surveillance in radar networks

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图 3 闭环要素耦合关系

Figure 3. The coupling mechanisms of the core Closed-Loop components

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图 4 雷达探测群协同架构发展历程

Figure 4. Evolution of the architecture for autonomous collaborative surveillance in radar networks

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图 5 协同感知技术发展历程

Figure 5. Evolution of collaborative sensing in radar networks

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图 6 协同感知数据处理流程

Figure 6. Data processing chain for collaborative sensing

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图 7 决策控制发展历程

Figure 7. Evolution of collaborative decision-making and control in radar networks

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图 8 协同决策控制模型

Figure 8. Collaborative decision-making and control model in radar networks

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图 9 协同演化的闭环工作流程

Figure 9. Closed-Loop model for collaborative evolution in radar networks

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表 1 雷达协同范式对比分析

Table 1. The comparative analysis of radar collaborative paradigms

协同范式	核心特征	智能水平	网络化程度	物理分布形态	关联关系
自主协同雷达探测群	弹性可重构架构，分布式自主决策，跨层级控制闭环，持续智能进化	体系智能	深度协同网络	分布式、弹性可重构	深度整合其他范式的核心能力，具备体系智能与持续进化能力。
分布式雷达	孔径合成，空间分集，灵活机动	基础智能	中等网络化	分布式	构成了自主协同的弹性可重构的物理分布与组网基础。
MIMO雷达	波形多样，虚拟阵列，高自由度	信号智能	可网络化	共址或分布式	其波形协同与高自由度处理能力，为自主协同提供了实现信号层精密协同的关键技术手段。
认知雷达	感知-行动循环，闭环学习，系统自适应	认知智能	可网络化	灵活可重构	为自主协同注入了“感知-行动”闭环与学习能力，是其智能决策与自主进化能力的核心来源。
网络化雷达	信息融合，资源共享，协同优化	规则智能	高度网络化	分层模块化	提供了信息交互与资源调度的网络化基础设施，是技术演进的起点与对比基线。

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表 2 典型的雷达探测群协同架构

Table 2. Typical architecture for autonomous collaborative surveillance in radar networks

架构类型	核心特征	主要优势	主要挑战	适用场景
集中式协同架构	1.中心节点统一调度与决策 2.全局信息感知与资源优化 3.结构层级清晰	1.全局寻优能力强，协同效率高 2.管控实现简单，决策一致性好 3.技术成熟度高，工程可实施性强	1.单点故障风险高，系统容错能力弱 2.通信带宽需求高，响应时延长 3.扩展性差，节点动态增删能力弱	任务模式可预定义的小规模协同探测
分布式协同架构	1.节点决策自治 2.控制与功能去中心化部署 3.基于局部交互的分布式协商	1.鲁棒性强，单点失效风险低 2.扩展性好，节点可动态加入/退出 3.通信与计算负载分散	1.协同一致性保障困难 2.通信开销大，收敛速度慢 3.动态负载均衡与资源调度复杂	大规模、广域部署的雷达网络
柔性可重构协同架构	1.任务/事件驱动 2.系统拓扑、资源、功能可动态重组	1.环境与任务适应能力强 2.资源按需分配，利用效率高 3.系统具备良好的弹性与韧性	1.重构机制复杂，实时保障难度大 2.动态资源调度与任务迁移复杂 3.对通信与计算资源要求高	1.多任务复杂场景 2.异构平台协同探测
智能化协同架构	1.以AI为核心驱动引擎 2.具备自主认知与协同决策能力	1.处理复杂不确定性问题能力强 2.支持动态非线性协同优化 3.具备数据驱动学习演进能力	1.强数据依赖，高质量样本获取困难 2.模型可解释性差，决策可信度存疑 3.计算开销大，实时部署门槛高	1.极端复杂电磁对抗环境 2.多目标、高机动目标的智能跟踪与识别
其他架构	探索跨学科协同新范式，突破传统架构局限	1.提供突破性协同思路与创新方案 2.可解决传统范式难以处理的复杂问题	1.理论成熟度不高 2.缺乏工程化实践经验与标准 3.与现有装备体系集成难度大	特定场景的复杂协同问题，如超大规模资源分配

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表 3 协同决策控制层级对比分析

Table 3. Hierarchical comparison of collaborative decision-making and control

协同层级	决策粒度	控制对象	时间尺度	核心目标
任务规划	粗粒度	探测任务/子任务	分钟级/秒级	任务分解与节点分配，实现任务覆盖
资源调度	中粒度	探测、计算、网络、存储等资源	秒级/百毫秒级	多节点资源协同配置，实现任务意图与资源能力的精准匹配
行动控制	细粒度	波束，波形，时序，平台位姿等	毫秒级/微秒级	底层动作的时空同步与参数匹配，实现执行协同

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表 4 协同资源调度方法对比分析

Table 4. Comparative analysis of collaborative resource scheduling algorithms in radar networks

决策方法	核心特征	典型算法	主要优势	主要挑战	适用场景
规则驱动	基于预设逻辑规则一致性或互补性决策	状态机、专家系统、决策树、协同预案	可解释性强、实时性高、实现简单	适应性差、存在规则冲突、维护困难	逻辑简单、实时性要求高、确定性环境
模型驱动	形式化数学模型，通过仿真与优化求解	动态规划、博弈论、最优控制、凸优化	理论性能优、全局最优、可处理不确定性	计算复杂度高、模型失配敏感、实时性差	多阶段决策、对抗博弈、理论最优解求解
任务驱动	以任务效用最大化为目标	组合优化、合同网协议	任务目标明确、系统效能高、支持动态重规划、可扩展性好	任务建模难、存在不可行解、受任务参数变化影响大	任务分配、多目标跟踪
资源驱动	以资源最优利用为目标	凸优化、雷达方程、对偶分解	资源利用率高、与硬件结合紧、可扩展性强	多维资源联合优化难、对环境变化响应慢	功率管理、波形捷变、波束调度
数据驱动	端到端机器学习	深度学习、强化学习	自适应强、能处理高维数据、端到端优化	样本数据数量和质量要求高、可解释性差、训练不稳定	复杂非线性环境、高维状态空间、在线快速推理

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表 5 协同行动控制算法对比分析

Table 5. Comparative analysis of collaborative action control algorithms in radar networks

名称	核心特征	典型算法	主要优势	主要挑战	适用场景
一致性协同控制	使所有节点状态渐近趋同	平均一致性、二阶一致性、事件触发一致性	简单易实现、通信开销小、理论成熟	对干扰敏感、通信延迟影响大、线性假设局限	时钟同步、分布式平均共识、数据融合、线性近似有效系统
鲁棒协同控制	在不确定/扰动下保持稳定与一致	H∞、滑模控制、扰动观测控制	抗干扰能力强、可靠性高、适用范围广	设计复杂、可能产生抖振、需不确定性边界信息	未知扰动环境、模型参数不确定系统
非线性协同控制	针对非线性系统实现复杂协同任务	反馈线性化、反步法、滑模控制、智能控制	贴合实际物理系统、控制性能优、灵活性高	设计难度大、稳定性证明困难、对模型精度要求高	非完整约束系统、复杂机械系统、强非线性耦合系统

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表 6 协同演化算法对比分析

Table 6. Comparative analysis of collaborative evolution algorithms in radar networks

名称	核心特征	典型算法	主要优势	主要挑战	适用场景
基于种群的协同演化	通过多个种群的协同进化优化雷达探测策略	遗传算法、粒子群、蚁群、人工蜂群等	全局搜索能力强，能有效避免局部最优解	计算复杂度高，收敛速度慢，参数设置敏感，难以处理实时性高的场景	简单场景下的探测任务优化及全局最优解要求高的场景
基于博弈的协同演化	将演化过程建模为多方博弈，通过策略互动实现系统均衡	非合作博弈、联盟博弈、扩展式博弈	可解释性强，策略均衡性好，鲁棒性与可扩展性强	模型设计复杂，均衡求解计算开销大，部分观测下性能下降	对抗性电子战环境，多雷达系统间存在竞争与合作的场景
基于多智能体的协同演化	将雷达系统建模为多智能体系统，通过智能体间的交互与学习实现策略优化	强化学习、分布式优化	分布式处理能力突出，适应性强，可快速响应环境变化，可扩展性好	通信开销大，非平稳环境下学习收敛慢，智能体间信任机制设计困难	动态环境下实时响应要求高的场景

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