多功能雷达干扰决策技术研究进展

doi:10.12305/j.issn.1001-506X.2026.11.12

系统工程与电子技术 ›› 2026, Vol. 48 ›› Issue (11): 3799-3810.doi: 10.12305/j.issn.1001-506X.2026.11.12

多功能雷达干扰决策技术研究进展

周青松¹(), 钱佳龙²^,³, 李宁¹, 李志汇¹, 黄超¹()

1. 国防科技大学电子对抗学院，安徽合肥 230037
2. 中国电子科技集团公司第三十六研究所，浙江嘉兴 314033
3. 电磁空间安全全国重点实验室，浙江嘉兴 314033

收稿日期:2025-09-26 修回日期:2026-02-07 接受日期:2026-04-07 出版日期:2026-10-25 发布日期:2026-05-29
通讯作者: 黄超 E-mail:zhouqingsong17@nudt.edu.cn;18226658179@163.com
作者简介:周青松（1982—），男，副教授，博士，主要研究方向为凸优化理论、稀疏理论
钱佳龙（1999—），男，硕士，主要研究方向为雷达干扰决策、强化学习
李　宁（1998—），男，研究生，主要研究方向为雷达干扰决策
李志汇（1991—），男，副教授，博士，主要研究方向为雷达与雷达对抗技术
基金资助:
国家自然科学基金（62301581）；中国博士后科学基金第73批面上项目（2023M734313）资助课题

Research progress on multi-functional radar jamming decision technology

Qingsong Zhou¹(), Jialong Qian²^,³, Ning Li¹, Zhihui Li¹, Chao Huang¹()

1. College of Electronic Engineering，National University of Defense Technology，Hefei 230037，China
2. The 36th Research Institute of China Electronic Technology Group Corporation，Jiaxing 314033，China
3. National Key Laboratory of Electromagnetic Space Security，Jiaxing 314033，China

Received:2025-09-26 Revised:2026-02-07 Accepted:2026-04-07 Online:2026-10-25 Published:2026-05-29
Contact: Chao Huang E-mail:zhouqingsong17@nudt.edu.cn;18226658179@163.com

摘要/Abstract

摘要：

多功能雷达（multi-functional radar, MFR）具有响应速度快、波束控制灵活、抗干扰能力强等优点，通过多维参数调节可实现搜索、跟踪、识别、制导等多种功能，这对干扰机的干扰决策提出巨大挑战。为有效应对MFR的智能化和多功能化，作为雷达对抗方，深入研究对MFR的智能干扰决策技术具有重要意义。首先，介绍几种传统的基于先验知识的雷达干扰决策技术，并指出其在对抗MFR时面临的不足。随后，从完成不同任务的角度出发，详细介绍智能干扰决策技术在干扰样式决策、干扰参数优化和干扰资源分配方面的应用。将强化学习与雷达干扰决策技术相结合，有助于克服传统干扰决策方法过度依赖先验知识的局限性，使系统能够学习MFR的内部行为规律，并在复杂电磁环境中不断更新干扰策略。最后，展望MFR智能干扰决策技术的发展趋势，强调随着人工智能和机器学习的进步，未来的干扰决策系统将更加智能化、自动化，为下一代智能干扰机的研制提供坚实的理论基础。

关键词: 多功能雷达, 雷达对抗, 干扰决策, 强化学习

Abstract:

The multi-functional radar （MFR） offers advantages such as fast response speed, flexible beam control, and strong anti-jamming capabilities. By adjusting multiple parameters, it can perform a variety of functions including search, tracking, identification, and guidance, posing significant challenges to the jamming decision-making of electronic countermeasures. To effectively counter the intelligence and multifunctionality of MFR, it is crucial for radar adversaries to conduct in-depth research into intelligent jamming decision-making technologies for MFR. First, several traditional radar jamming decision-making techniques based on prior knowledge are introduced and their limitations when countering MFR are highlighted. Subsequently, from the perspective of accomplishing different tasks, a detailed introduction is provided to the application of intelligent jamming decision-making technologies in three areas: jamming pattern decision-making, jamming parameter optimization, and jamming resource allocation. Integrating reinforcement learning with radar jamming decision-making technologies helps overcome the limitations of traditional methods that overly rely on prior knowledge. This integration enables the system to learn the internal behavior patterns of MFR and continuously update jamming strategies in complex electromagnetic environments. Finally, the development trends of intelligent jamming decision-making technologies for MFR are explored, emphasizing that with the advancements in artificial intelligence and machine learning, future jamming decision systems will become more intelligent and automated, providing a solid theoretical foundation for the development of next-generation intelligent jammers.

Key words: multi-functional radar, radar countermeasure, jamming decision, reinforcement learning

中图分类号:

TN 974

周青松, 钱佳龙, 李宁, 李志汇, 黄超. 多功能雷达干扰决策技术研究进展[J]. 系统工程与电子技术, 2026, 48(11): 3799-3810.

Qingsong Zhou, Jialong Qian, Ning Li, Zhihui Li, Chao Huang. Research progress on multi-functional radar jamming decision technology[J]. Systems Engineering and Electronics, 2026, 48(11): 3799-3810.

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雷达方策略	干扰方策略
雷达方策略	$ {J}_{1} $	$ {J}_{2} $	$ {J}_{3} $	$ {J}_{4} $
$ {R}_{1} $	$ {e}_{11} $	$ {e}_{12} $	$ {e}_{13} $	$ {e}_{14} $
$ {R}_{2} $	$ {e}_{21} $	$ {e}_{22} $	$ {e}_{23} $	$ {e}_{24} $
$ {R}_{3} $	$ {e}_{31} $	$ {e}_{32} $	$ {e}_{33} $	$ {e}_{34} $

强化学习模型	雷达干扰决策模型
智能体	干扰机
环境	包含目标雷达的电磁环境
状态	侦察得到环境信息
动作	施加干扰
奖励	在线干扰效果评估

决策方法	核心改进
原始QL算法	引入CEW框架，优化智能干扰决策流程
基于最大期望加权估计的干扰决策方法	结合最大期望加权估计与时间漂移方法，适应快速变化的雷达策略
改进QL算法（模拟退火+梯度下降）	通过Metropolis准则增强探索，梯度下降加速收敛
Dyna-Q算法	采用规划步数自适应Dyna-Q来提升样本利用率
基于先验知识的QL算法	通过先验知识设计收益函数，缩短训练时间
启发式规划QL算法	利用势能函数构造奖励函数，避免局部最优
混合改进算法（蚁群+QL/双层强化学习）	融合启发式算法或分层框架，提升复杂场景适应性
DQN算法	提升大规模状态空间情况下的决策实时性与准确率
DDQN算法	减少Q值过估计，提升干扰策略稳定性
D3QN算法	分离值函数与优势函数，增强对未知雷达模式的认知
A2C算法	策略梯度与值函数结合，兼具策略探索与利用优势
A3C算法	多线程异步训练，决策时间减少超过70%
SAC算法	引入环境建模与负激活Relu，提升参数利用率与干扰适应性

参数优化类型	算法	方法特性	优化目标
间歇采样转发干扰参数优化	QL算法	结合QL算法与“切割”假设法，自适应调整采样时间和转发时间	提高干扰信号与雷达信号的匹配度
非均匀间歇采样干扰参数优化	双层强化学习（QL算法+DQN算法）	外层QL算法选择干扰参数范围，内层DQN算法优化采样时间和转发次数	降低脉压后干扰信号幅度的标准差与均值的比值
对抗频率捷变雷达干扰参数优化	QL算法	结合QL算法优化频率和脉冲宽度，以干信比和功率消耗为评估指标	提高干信比，减少干扰资源消耗
对抗频率捷变雷达干扰参数优化	PPO算法	利用PPO算法结合拦截、发射、评估奖励函数优化频率选择	提升对抗场景下的频率自适应能力
对抗脉内频率捷变雷达干扰参数优化	GA-Dueling DQN算法	结合注意力机制与强化学习，学习雷达频率变化规律	提高干信比，抑制脉内频率捷变雷达的干扰

决策方法	核心算法	方法特性	优化目标
分层强化学习	Dual QL算法	分解动作空间为样式和参数子空间，构建两层交互式QL模型	最小化雷达威胁等级，实现样式和时频域参数联合优化
	QL算法+DDPG算法	外层QL优化干扰样式，内层DDPG优化连续脉冲参数，结合AHP-TOPSIS评估效果	同步优化样式和参数，提升干扰效率和资源利用率
	QL算法+JBSD算法	采用QL算法优化干扰样式，JBSD算法优化干扰参数	同步优化样式和参数，提升干扰效率和资源利用率
混合动作空间处理	PDQN算法，HPPO算法	使用混合动作空间强化学习算法并行输出离散的干扰样式和连续的干扰参数	提高混合动作空间的决策稳定性和训练速度
干扰参数离散化处理	启发式QL算法、D3QN算法	离散化干扰参数与样式组合，采用D3QN或启发式QL算法	在统一优化框架下实现干扰样式与参数联合优化

多功能雷达干扰决策技术研究进展

Research progress on multi-functional radar jamming decision technology

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干扰资源分配方法	分配目标
QL、DDQN、DQN、结合先验知识的DQN算法	根据突防航距和雷达状态动态选择干扰目标及样式，提升突防效能
基于优先级经验重放的DDQN算法	克服数据相关性，减少迭代次数，提高干扰决策效率
改进QL算法	以组网雷达威胁程度最小化为指标，实现干扰目标与样式的在线分配
状态预测网络结合SAC算法	解决多无人机突防时的价值评估偏差，提升干扰策略训练稳定性
改进DDQN算法（和树结构+HRL）	针对频率捷变雷达，动态分配干扰任务与参数，优化频域协同干扰效能
PPO算法	联合优化干扰机编队航路点与功率分配，以突防任务完成度和突防距离为收益
SP-HASAC算法	通过软参数共享机制提高训练效率，同时保留各智能体的独立决策能力
分层仿生强化学习算法	联合优化干扰任务与功率分配，适应组网雷达检测融合动态特性，缩短决策时间
进化强化学习算法	引入进化算法的种群优选机制辅助策略搜索，有效避免多机协同训练陷入局部最优
MADDPG、LSTM-DDPG算法	针对雷达搜索/跟踪状态，分别设计协同干扰策略与航迹欺骗策略
QMIX算法	针对值分解问题，通过混合网络拟合联合Q值函数，通过最大化局部Q值实现协作场景的全局最优

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