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红外空空导弹抗干扰效能评估建模

牛得清 伍友利 徐洋 吴鑫 张丹旭 杨鹏飞

牛得清, 伍友利, 徐洋, 等 . 红外空空导弹抗干扰效能评估建模[J]. 北京航空航天大学学报, 2021, 47(9): 1874-1883. doi: 10.13700/j.bh.1001-5965.2020.0334
引用本文: 牛得清, 伍友利, 徐洋, 等 . 红外空空导弹抗干扰效能评估建模[J]. 北京航空航天大学学报, 2021, 47(9): 1874-1883. doi: 10.13700/j.bh.1001-5965.2020.0334
NIU Deqing, WU Youli, XU Yang, et al. Modeling of anti-jamming effectiveness evaluation of infrared air-to-air missile[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(9): 1874-1883. doi: 10.13700/j.bh.1001-5965.2020.0334(in Chinese)
Citation: NIU Deqing, WU Youli, XU Yang, et al. Modeling of anti-jamming effectiveness evaluation of infrared air-to-air missile[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(9): 1874-1883. doi: 10.13700/j.bh.1001-5965.2020.0334(in Chinese)

红外空空导弹抗干扰效能评估建模

doi: 10.13700/j.bh.1001-5965.2020.0334
详细信息
    通讯作者:

    伍友利, E-mail: wadebae@163.com

  • 中图分类号: TN976;TJ762.23

Modeling of anti-jamming effectiveness evaluation of infrared air-to-air missile

More Information
  • 摘要:

    为了摸清红外空空导弹性能、提高导弹作战效能,需要全面有效地对导弹抗干扰能力进行评估。但是受限于无穷多的对抗情况,目前多数基于典型对抗场景进行研究分析,不够全面。为此使用改进的拉丁超立方采样法在全范围内设计采样点。首先,对红外对抗原理和仿真系统进行说明和构建,确定输入参数范围和类型;其次,对拉丁超立方采样进行改进优化,并将其生成的采样结果按需离散化,满足诱饵离散型参数设置需求;最后,运用上述生成的初始参数组合运行仿真系统,将获取的数据作为样本集交给随机森林模型学习,通过调优参数及调整损失矩阵后,得到预测精度为90.4%的红外空空导弹抗干扰效能评估模型。通过仿真,验证了所提模型在不同红外对抗态势和不同提取误差下的有效性。

     

  • 图 1  红外诱饵干扰过程

    Figure 1.  Infrared decoy jamming process

    图 2  比例导引法

    Figure 2.  Proportional navigation guidance

    图 3  仿真系统框架

    Figure 3.  Simulation system framework

    图 4  拉丁方阵及采样

    Figure 4.  Latin square and sampling

    图 5  拉丁超立方采样结果

    Figure 5.  Latin hypercube sampling results

    图 6  示例采样过程

    Figure 6.  Sample sampling procedure

    图 7  导引头视场图

    Figure 7.  Seeker view image

    图 8  存在提取误差情况下评估模型相对误差变化

    Figure 8.  Relative error graph of evaluation model with extraction error

    表  1  图像特征定义

    Table  1.   Image feature definition

    特征编号 特征 定义
    c1 能量
    c2 能量变化率
    c3 平均灰度 c3=c1/s
    c4 长宽比 c4=l/w
    注:m1×n1为导引头图像像素数;Ei, j为像素点能量;s为目标图像区域像素点个数;lw分别为目标图像区域外接矩形的长和宽。
    下载: 导出CSV

    表  2  性能对比结果

    Table  2.   Performance comparison results

    方法 规模m×n 平均值Maximin 平均值Φp 计算时间/s
    lhs 50 000×12 0.189 6.328 44.28
    TPLHD 50 000×12 0.323 5.559 46.32
    下载: 导出CSV

    表  3  随机森林模型结果

    Table  3.   Random forest model results

    预测模型 模型1 模型2
    P1 P2 P3 P1 P2 P3
    T1 35 505 114 110 32 336 604 2 789
    T2 953 4 364 130 403 4 533 511
    T3 1 915 375 6 534 324 164 8 336
    Accuracy/% 92.8 92.8 90.4 90.4 90.4 90.4
    PPV or FDR of T1/% 93 2 2 98 11 24
    PPV or FDR of T2/% 2 90 2 1 86 4
    PPV or FDR of T3/% 5 8 96 1 3 72
    PPV/% 93 90 96 98 86 72
    FDR/% 7 10 4 2 1 28
    注:PPV or FDR of T1为实际情况第一级的PPV或者FDR。
    下载: 导出CSV

    表  4  对抗场景设置

    Table  4.   Countermeasure scenario setting

    参数 场景a 场景b 场景c
    目标机动类型
    首枚诱饵发射时间/s 1 1 1.5
    诱饵总数 12 24 48
    每组数量 12 4 6
    组间间隔/s 0.6 0.8 1
    组内间隔/s 0.1 0.1 0.2
    每次投掷数量 2 1 2
    水平投掷角/(°) 60 60 30
    垂直投掷角/(°) 60 -60 -30
    诱饵投掷速度/(m·s-1) 10 30 40
    导弹水平进入角/(°) 85 10 160
    初始弹目距离/m 2 500 3 500 4 500
    预测结果 Level 3 Level 2 Level 1
    仿真命中率/% 29 64 96
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-07-13
  • 录用日期:  2020-09-18
  • 网络出版日期:  2021-09-20

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