改进群智能算法多目标干扰决策

doi:10.3969/j.issn.1009-086x.2020.01.017

现代防御技术 ›› 2020, Vol. 48 ›› Issue (1): 107-112.DOI: 10.3969/j.issn.1009-086x.2020.01.017

• 仿真技术 • 上一篇

改进群智能算法多目标干扰决策

陈奕琪

电子科技大学信息与通信工程学院,四川成都 611731

收稿日期:2019-06-20 修回日期:2019-09-02 出版日期:2020-02-20 发布日期:2021-01-20
作者简介:陈奕琪(1996-),女,江苏连云港人。博士生,主要研究方向为电子对抗,信号与信息处理。通信地址:100048 北京838信箱 E-mail:201821011501@std.uestc.edu.cn

Improved Swarm Intelligence Algorithm for Multi-Objective Jamming Decision

CHEN Yi-qi

University of Electronic Science and Technology of China,School of Information and Communication Engineering, Sichuan Chengdu 611731,China

Received:2019-06-20 Revised:2019-09-02 Online:2020-02-20 Published:2021-01-20

摘要/Abstract

摘要： 如何运用有限的干扰资源获得最大的干扰效益是电子对抗研究的重点技术之一,针对协同电子对抗,提出一种最优干扰决策方法,解决对抗资源和雷达目标数量不等的干扰资源分配问题。围绕组网雷达检测概率和定位精度2个评估指标,建立基于多目标优化的协同干扰决策任务模型。针对传统人工蜂群和蚁群算法流程寻优缓慢的问题,在候选解的搜索中自适应地增加与当次迭代最优解的交叉运算,给出两改进算法对模型的通用求解步骤,通过仿真验证算法提高了收敛速度。

关键词: 协同干扰, 资源分配, 人工蜂群算法, 蚁群算法, 多目标优化, 干扰效益

Abstract: How to use limited resources to obtain maximum benefits is one of the focuses in electronic warfare. For multi-target collaborative electronic countermeasures, an optimism assignment method of jamming resource is put forward, to solve the problem that jamming resource is generally not equal to the radar number. Aiming at the detection probability and positioning accuracy of the netted radar, a jamming decision task model based on multi-objective optimization is established. To solve the problem that the convenience rates of traditional artificial bee colony algorithm and ant colony algorithm are slow, crossover operation with optimal solution to current iteration is adaptively added, the general improvement strategy is put forward, and through the simulation experiment, the better rate of convergence of the algorithm is proved.

Key words: cooperative jamming, resource assignment, artificial bee colony algorithm, ant colony algorithm, multi-objective optimization, jamming efficiency

中图分类号:

陈奕琪. 改进群智能算法多目标干扰决策[J]. 现代防御技术, 2020, 48(1): 107-112.

CHEN Yi-qi. Improved Swarm Intelligence Algorithm for Multi-Objective Jamming Decision[J]. Modern Defense Technology, 2020, 48(1): 107-112.

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改进群智能算法多目标干扰决策

Improved Swarm Intelligence Algorithm for Multi-Objective Jamming Decision

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