群智通信对抗环境下的认知干扰频谱资源决策方法

戴进; 童晓兵; 冯智斌; 徐逸凡; 孙玉洁; 贺兴; 战志远; 许江

doi:10.12382/bgxb.2025.0173

您当前的位置：

首页 >

文章列表页 >

群智通信对抗环境下的认知干扰频谱资源决策方法

更新时间：2026-04-24

- 群智通信对抗环境下的认知干扰频谱资源决策方法
- Decision-making Method of Cognitive Jamming Spectrum Resource in a Crowd Intelligence Communication Countermeasure Environment
- 兵工学报 2026年47卷第1期页码：250173
- 作者机构：
  
  陆军工程大学通信工程学院，江苏南京 210007
  北方自动控制技术研究所，山西太原 030006
  31700 部队，辽宁沈阳 110000
  天津安力信通讯科技有限公司，天津 300380
- 作者简介：
  
  *通信作者邮箱：fengzb1995@163.com
- 基金信息：
  
  国家自然科学基金项目(62401625)
- DOI：10.12382/bgxb.2025.0173
  中图分类号： TN975
- 收稿：2025-03-12，
  
  网络首发：2026-02-11，
  
  纸质出版：2026-01-31
- 稿件说明：
移动端阅览
戴进, 童晓兵, 冯智斌, 等. 群智通信对抗环境下的认知干扰频谱资源决策方法[J]. 兵工学报, 2026,47(1):250173.

DAI Jin, TONG Xiaobing, FENG Zhibin, et al. Decision-making Method of Cognitive Jamming Spectrum Resource in a Crowd Intelligence Communication Countermeasure Environment[J]. Acta Armamentarii, 2026, 47(1): 250173.
戴进, 童晓兵, 冯智斌, 等. 群智通信对抗环境下的认知干扰频谱资源决策方法[J]. 兵工学报, 2026,47(1):250173. DOI： 10.12382/bgxb.2025.0173.

DAI Jin, TONG Xiaobing, FENG Zhibin, et al. Decision-making Method of Cognitive Jamming Spectrum Resource in a Crowd Intelligence Communication Countermeasure Environment[J]. Acta Armamentarii, 2026, 47(1): 250173. DOI： 10.12382/bgxb.2025.0173.

摘要

现代化战争中，群智通信对抗已成为认知电子战的重要发展趋势。现有认知干扰手段主要聚焦于单目标对抗场景，随着敌方目标设备抗干扰能力的提升和规模的扩大，难以达到理想的干扰效果。然而多干扰机在协同过程中内部关系耦合复杂且往往存在决策冲突，导致干扰频谱资源浪费、协作效率低下，无法满足干扰的时效性需求。针对此问题，提出群智通信对抗环境下的认知干扰频谱资源博弈决策架构及方法，在博弈论和机器学习理论方法的指导下，围绕协同干扰博弈决策模型与效果评估机理、面向瞬时均衡收益的干扰高效决策压制以及面向长期累积收益的干扰稳健决策占优等多个方面，对关键技术展开论述，通过构建“内部协同、外部对抗”博弈模型与间接效果评估机制，缓解反馈缺失问题，并结合合作式多智能体学习提升联合决策能力。新方法在压制能力、协同效率和策略稳定性上均优于现有方法。

Abstract

In modern warfare

the crowd intelligence communication countermeasure has become an important development trend of cognitive electronic warfare. The existing cognitive jamming methods mainly focus on the single target countermeasure scenario

which is difficult to achieve the ideal jamming effect with the improvements in the anti-jamming ability of enemy target equipment and the expansion of its scale. However

the internal coupling relationship of multiple jammers during coordinative process is complicated and there exists the conflicts in decision-making

thus leading to the waste of jamming spectrum resources and the low cooperation efficiency

which could not meet the timeliness requirements. To address the problem

this paper proposes a game decision-making architecture and method of cognitive jamming spectrum resource in a crowd intelligence communication countermeasure environment. Guided by the theories and methods of game theory and machine learning

it delves into much key technical aspects: crowd jamming game decision-making models and effect evaluation mechanism

jamming-efficient decision-making suppression for instantaneous equilibrium gains

and jamming robust decision-making dominance geared towards long-term accumulative gains. The absence of feedback is mitigated by constructing an “ internal collaboration and external confrontation ” game-theoretic model and an indirect effectiveness evaluation mechanism

and the joint decision-making capability is enhanced by a cooperative multi-agent learning framework. The proposed method outperforms the existing methods in jamming suppression capability

coordination efficiency

and strategy stability.

关键词

Keywords

references

王健，杨渡佳，黄科举，等.认知电子战发展趋势：从单体智能到群体智能[J].信息对抗技术，2023,2（4/5）:151-170.

WANG J, YANG D J, HUANG K J, et al.Development trends in cognitive electronic warfare:from individual intelligence to swarm intelligence[J].Information Countermeasure Technology,2023, 2（4/5）:151-170.（in Chinese）

KONSTANTINOS P, MARIOS I, SRIKANTH V K. Denial of service attacks in wireless networks:the case of jammers[J]. IEEE Communications Surveys & Tutorials,2011,13（2）:245-257.

HAN Z, NIYATO D, SAAD W, et al. Game theory in wireless and communication networks: theory, models, and applications [M]. Cambridge, UK:Cambridge University Press,2011.

BAI S, SHAO H Z, LIN J R, et al. Jamming the relay-assisted multi-user wireless communication system: a zero-sum game approach [J]. IEEE Transactions on Information Forensics and Security,2023,18:3198-3211.

WANG H C, WANG Q, CHEN Q. Opponent's dynamic prediction model-based power control scheme in secure transmission and smart jamming game[J]. IEEE Internet of Things Journal,2024, 11（2）:2870-2882.

WANG H C, DING G R, CHEN J, et al. UAV anti-jamming communications with power and mobility control [J]. IEEE Transactionson Wireless Communications, 2023, 22（7）: 4729-4744.

TANG G F, CAI Y F, GAN R B, et al. Evaluation of radar jamming effect [M]∥Techniques and System Design of Radar Active Jamming. Singapore:Springer,2023:307-333.

KIM Y S, LEE H J. On classifying, evaluating the effect of jamming attacks[C]∥Proceedings of the 24th Edition of the International Conference on Information Networking. Piscataway, NJ, US:IEEE, 2010:27-29.

韩晨，刘爱军，牛英滔，等.智能干扰：目的、方式、反馈[J].指挥与控制学报，2022,8（2）:133-140.

HAN C, LIU A J, NIU Y T, et al.Intelligent interference:purpose, method, and feedback[J].Journal of Command and Control,2022, 8（2）:133-140.（in Chinese）

SHAVBO S, HAO Z, MEDHAT H M E, et al. Smart jamming attack and mitigation on deep transfer reinforcement learning enabled resource allocation for network slicing [J]. IEEE Transactions on Machine Learning in Communications and Networking,2024,2:1492-1508.

RAO N, XU H, QI Z S, et al. Fast adaptive jamming resource allocation against frequency-hopping spread spectrum in wireless sensor networks via meta-deep-reinforcement-learning[J]. IEEE Transactions on Aerospace and Electronic Systems,2024,60（6）:7676-7693.

RAO N, XU H, QI Z S, et al. Adaptive jamming decision-making against FHSS communications via inexpert demonstrations assisted meta reinforcement learning[J]. IEEE Communications Letters, 2025,29（1）:105-109.

WANG L G, SONG F, FENG Z B, et al. Intelligent jamming against dynamic spectrum access user: algorithm design and verification system implementation [J]. IEEE Wireless Communications Letters,2022,11（11）:2405-2409.

CHARLES E T, BUEHRER R M. Linear jamming bandits:sample-efficient learning for non-coherent digital jamming[C]∥Proceedings of Military Communications Conference. Washington, D. C. ,US:IEEE,2022:198-203.

夏利群.基于深度强化学习和博弈理论的认知干扰机策略优化方法研究[D].北京：军事科学院，2024.

XIA L Q.Research on optimization method of cognitive jamming machine strategy based on deep reinforcement learning and game theory[D].Beijing: Academy of Military Science, 2024.（in Chinese）

刘林奇.基于多域联合表征学习的通信干扰波形生成技术研究[D].北京：北京邮电大学，2024.

LIU L Q.Research on communication jamming waveform generation technology based on multi-domain joint representation learning [D].Beijing:Beijing University of Posts and Telecommunications, 2024.（in Chinese）

周成，林茜，马丛珊，等.通信干扰信道和功率智能决策算法[J].电子与信息学报，2024,46（10）:3957-3965.

ZHOU C, LIN Q, MA C S, et al.Intelligent decision algorithm for communication jamming channel and power [J].Journal of Electronics and Information Technology, 2024, 46（10）: 3957-3965.（in Chinese）

WANG X M, WANG J L, XU Y H. Dynamic spectrum antijamming communications:challenges and opportunities[J]. IEEE Communications Magazine,2020,58（2）:79-85.

WANG L G, LI G X, SONG F, et al. A DRL-based intelligent jamming approach for joint channel and power optimization[J]. Wireless Communications and Mobile Computing, 2023:6678991.

饶宁，许华，蒋磊，等.基于多智能体深度强化学习的分布式协同干扰功率分配算法[J].电子学报，2022,50（6）:1319-1330.

RAO L, XU H, JIANG L, et al.Distributed collaborative interference power allocation algorithm based on multi-agent deep reinforcement learning [J].Acta Electronica Sinica, 2022, 50（6）:1319-1330.（in Chinese）

宋佰霖，许华，齐子森，等.一种基于深度强化学习的协同通信干扰决策算法[J].电子学报，2022,50（6）:1301-1309.

SONG B L, XU H, QI Z S, et al.A collaborative communication interference decision-making algorithm based on deep reinforcement learning[J].Acta Electronica Sinica,2022,50（6）:1301-1309.（in Chinese）

黄大通，邢世其，徐伟，等.对多通道SAR欺骗干扰抑制的多干扰机协同调幅对抗方法[J].电子学报，2023,51（9）:2301-2312.

HUANG D T, XING S Q, XU W, et al.A Multi-jammer coordinated amplitude modulation countermeasure method for suppressing deception jamming in multi-channel SAR[J].Acta Electronica Sinica,2023,51（9）:2301-2312.（in Chinese）

彭翔，许华，蒋磊，等.一种基于深度强化学习的动态自适应干扰功率分配方法[J].电子学报，2023,51（5）:1223-1234.

PENG X, XU H, JIANG L, et al.A Dynamic adaptive interference power allocation method based on deep reinforcement learning [J].Acta Electronica Sinica, 2023, 51（5）: 1223-1234.（in Chinese）

WANG L G, SONG F, FANG G, et al. A multi-agent reinforcement learning-based collaborative jamming system:algorithm design and software-defined radio implementation[J]. China Communications,2022,19（10）:38-54.

XING H X, XING Q H, WANG K. A joint allocation method of multi-jammer cooperative jamming resources based on suppression effectiveness[J]. Mathematics,2023,11（4）:1-25.

ZHANG C D, SONG Y Q, JIANG R D, et al. A cognitive electronic jamming decision-making method based on Q-learning and ant colony fusion algorithm [J]. Remote Sensing, 2023, 15（12）:1-21.

ANDREY G, ATHINA P, WADE T, et al. A multi-jammer game with latency as the user's communication utility [J]. IEEE Communications Letters,2020,24（9）:1899-1903.

AKEEL B, SATTAR B S. Decision making approaches in cognitive radio-status, challenges and future trends [C]∥Proceedings of 2020 International Conference on Advanced Science and Engineering. Washington, D. C. , US: IEEE, 2020:198-198.

YIN X Y, LIU Y Z, ZHANG E J, et al. An evaluation method for power multi-service 5G fusion application scheme based on fuzzy analytic hierarchy process[C]∥Proceedings of the 2022 4th International Conference on Frontiers Technology of Information and Computer. Washington, D. C. ,US:IEEE,2022:823-827.

LIU B H, SUN J T. Stackelberg game under asymmetric information in unmanned aerial vehicle swarm active deception defense:from a multi-layer network perspective[C]∥Proceedings of 2021 International Conference on Big Data and Intelligent Decision Making. Washington, D. C. ,US:IEEE,2021:75-79.

TONG K, WU J H, WANG J W, et al. Random placement algorithm of military game based on fisher-yates[C]∥Proceedings of the 2021 33rd Chinese Control and Decision Conference. Washington, D. C. ,US:IEEE,2021:2192-2196.

PENG N, DAI L. Multi-armed-bandit-based framed slotted aloha for throughput optimization [J]. IEEE Communications Letters, 2024,28（4）:847-851.

TRUNG V P, SHABNAM S, TRI G N. Q-transfer: a novel framework for efficient deep transfer learning in networking[C]∥Proceedings of 2020 International Conference on Artificial Intelligence in Information and Communication. Washington, D. C. ,US:IEEE,2020:146-151.

GAO H Y. Image classification based on dual-attention mechanism and multi-convolution layer[C]∥Proceedings of the 2022 4th International Conference on Communications, Information System and Computer Engineering. Washington, D. C. ,US:IEEE,2022 160-163.

浏览量

下载量

CNKI被引量

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据