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弹箭与制导学报 >

2014 , Vol. 34 >Issue 1: 60 - 65

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2014.01.025

弹药技术

超视距空战下空射诱饵弹协同接敌策略

  • 张涛 ,
  • 于雷 ,
  • 周中良 ,
  • 孔庆春
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  • 1 空军工程大学航空航天工程学院,西安 710038
    2 94831 部队,福建武夷山 354300
    3 西北工业大学航天学院,西安 710072

张涛(1982-),男,山东新泰人,博士研究生,研究方向:航空武器系统总体、仿真与控制。

收稿日期: 2013-04-05

  网络出版日期: 2025-05-29

基金资助

航空科学基金(20095196012);博士生创新基金(Dx2010106)

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Coordinated Engagement Strategy for MALD and Fighter in Beyond-visual-range Air Combat

  • ZHANG Tao ,
  • YU Lei ,
  • ZHOU Zhongliang ,
  • KONG Qingchun
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  • 1 Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038, China
    2 No. 94831 Unit, Fujian Wuyishan 354300, China
    3 School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China

Received date: 2013-04-05

  Online published: 2025-05-29

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摘要

以战斗机携带空射诱饵弹(MALD)进行超视距一对一空战为背景,基于滚动时域控制思想对战斗机及MALD协同接敌策略进行研究。建立战斗机、MALD及目标机动模型;在综合分析MALD欺骗函数、载机优势函数及协同函数的基础上,构建协同接敌目标函数;提出一种基于滚动时域-粒子群算法的战斗机及MALD协同接敌方法,最后进行仿真验证,结果表明该方法能有效利用MALD对目标进行欺骗,提高我方战斗机在空战过程中的生存性,提高对目标的攻击优势。

关键词： 空射诱饵弹; 协同接敌策略; 滚动时域; 粒子群算法; 变权重

本文引用格式

张涛 , 于雷 , 周中良 , 孔庆春 . 超视距空战下空射诱饵弹协同接敌策略[J]. 弹箭与制导学报, 2014 , 34(1) : 60 -65 . DOI: 10.15892/j.cnki.djzdxb.2014.01.025

Abstract

Considering a scenario of one-to-one air combat and miniature air-launched decoy (MALD) which is taken by fighter, a coordinated engagement strategy model for MALD and fighter based on receding horizon control (RHC) was established in order to solve the complicated problem. Dynamical models of fighter, MALD and target were built. Based on the analysis of the MALD bam function, fighter advantage function and coordinated function, the decision-making objective function was built. The method of coordinated engagement strategy of MALD and fighter based on RHC and particle swarm optimization (PSO) was proposed. Simulation results show that this algorithm can bam the target, improve the probability of survival and the advantage of attack.

Key words： miniature air-launched decoy; coordinated engagement strategy; receding horizon control; particle swarm optimization; variableweight

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