系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (5): 1385-1394.doi: 10.12305/j.issn.1001-506X.2025.05.01

• 电子技术 •    

基于椋鸟迁徙的干扰资源动态分配方法

阎潇, 王青平, 胡卫东, 朱虹宇, 王超   

  1. 国防科技大学电子科学学院, 湖南 长沙 410073
  • 收稿日期:2024-03-08 出版日期:2025-06-11 发布日期:2025-06-18
  • 通讯作者: 王青平
  • 作者简介:阎潇 (2000—), 男, 硕士研究生, 主要研究方向为智能电子对抗与评估、机器学习
    王青平 (1988—), 男, 讲师, 博士, 主要研究方向为微波毫米波制导对抗
    胡卫东 (1967—), 男, 教授, 博士, 主要研究方向为雷达信息处理、自动目标识别技术研究、多源信息融合
    朱虹宇 (1994—), 男, 博士研究生, 主要研究方向为模式识别、波形设计
    王超 (1977—), 男, 副教授, 博士, 主要研究方向为模式识别、波形设计
  • 基金资助:
    国家自然科学基金(62301570)

Dynamic interference resource allocation method based on starling migration

Xiao YAN, Qingping WANG, Weidong HU, Hongyu ZHU, Chao WANG   

  1. School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China
  • Received:2024-03-08 Online:2025-06-11 Published:2025-06-18
  • Contact: Qingping WANG

摘要:

针对无人机集群在有限通信资源约束下的协同干扰资源分配问题, 提出一种基于椋鸟迁徙的干扰资源动态分配算法。设计基于椋鸟迁徙原理的无人机自编组机制, 在较低的信息传输条件下, 具有较好的实时干扰决策效果, 极大缓解无人机集群内通信资源紧缺的问题。仿真结果表明, 在雷达中威胁场景下, 所提方法在保证干扰效果的条件下相较遗传算法和改进蚁群算法分别有88%和84.4%的通信信息量降低。在雷达高威胁场景下, 所提方法较遗传算法和遗传-蚁群算法降低了60.75%的信息传输需求的同时, 仍然具有88.75%和85.64%的干扰效果。

关键词: 椋鸟迁徙算法, 干扰资源分配, 干扰效果评估, 协同干扰

Abstract:

Aiming at the cooperative interference resource allocation problem of unmanned aerial vehicle (UAV) cluster under the constraint of limited communication resources, a dynamic interference resource allocation method is proposed based on starling migration. In this paper, a self-organizing mechanism of UAV based on the principle of starling migration is designed. Under the condition of low information transmission, it has better real-time interference decision-making effect, which greatly alleviates the shortage of communication resources in UAV cluster. The simulation results show that under the threat scenario of radar, compared with genetic algorithm and improved ant colony algorithm, this method has 88% and 84.4% reduction of communication information respectively under the condition of ensuring the interference effect. In the high-threat scenario of radar, this method reduces the information transmission requirement by 60.75% compared with genetic algorithm and genetic-ant colony algorithm, and still has 88.75% and 85.64% interference effects.

Key words: starling migration algorithm, interference resource distribution, evaluation of interference effectiveness, cooperative interference

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