Decision-making Method of Cognitive Jamming Spectrum Resource in a Crowd Intelligence Communication Countermeasure Environment

DAI Jin; TONG Xiaobing; FENG Zhibin; XU Yifan; SUN Yujie; HE Xing; ZHAN Zhiyuan; XU Jiang

doi:10.12382/bgxb.2025.0173

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Decision-making Method of Cognitive Jamming Spectrum Resource in a Crowd Intelligence Communication Countermeasure Environment

更新时间：2026-04-24

- Decision-making Method of Cognitive Jamming Spectrum Resource in a Crowd Intelligence Communication Countermeasure Environment
- Acta Armamentarii Vol. 47, Issue 1, Pages: 250173(2026)
- 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：
- 基金信息：
- DOI：10.12382/bgxb.2025.0173
  CLC： TN975
- Received：12 March 2025，
  
  Online First：11 February 2026，
  
  Published：31 January 2026
- 稿件说明：
移动端阅览
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：

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

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DAI Jin

TONG Xiaobing

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