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一种参数区间交叉类型的目标识别方法

李双明 关欣 赵静 吴斌

李双明, 关欣, 赵静, 等 . 一种参数区间交叉类型的目标识别方法[J]. 北京航空航天大学学报, 2020, 46(7): 1307-1316. doi: 10.13700/j.bh.1001-5965.2019.0442
引用本文: 李双明, 关欣, 赵静, 等 . 一种参数区间交叉类型的目标识别方法[J]. 北京航空航天大学学报, 2020, 46(7): 1307-1316. doi: 10.13700/j.bh.1001-5965.2019.0442
LI Shuangming, GUAN Xin, ZHAO Jing, et al. A methodology for target recognition with parameters of interval cross type[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1307-1316. doi: 10.13700/j.bh.1001-5965.2019.0442(in Chinese)
Citation: LI Shuangming, GUAN Xin, ZHAO Jing, et al. A methodology for target recognition with parameters of interval cross type[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1307-1316. doi: 10.13700/j.bh.1001-5965.2019.0442(in Chinese)

一种参数区间交叉类型的目标识别方法

doi: 10.13700/j.bh.1001-5965.2019.0442
基金项目: 

国防科技卓越青年科学基金 2017-JCJQ-ZQ-003

泰山学者工程专项经费 ts201712072

详细信息
    作者简介:

    李双明  男, 博士研究生。主要研究方向:目标识别技术

    关欣  女, 博士, 教授, 博士生导师。主要研究方向:信息融合、电子对抗及智能计算

    赵静  女, 博士。主要研究方向:证据理论

    吴斌  男, 博士研究生。主要研究方向:无线传感器网络

    通讯作者:

    关欣, E-mail:gxtongwin@163.com

  • 中图分类号: TN95;C934

A methodology for target recognition with parameters of interval cross type

Funds: 

National Defense Science and Technology Excellence Youth Talent Fund 2017-JCJQ-ZQ-003

Taishan Scholar Engineer-ing Special Fund ts201712072

More Information
  • 摘要:

    针对参数区间为交叉类型的目标识别问题,提出了基于直觉模糊集和云模型的逼近理想点(TOPSIS)识别方法。构建了包含个体类和交叉类的目标数据库模型,根据云模型的多步估计算法,得到未知目标相对已知目标类的确定度,提出了确定度向隶属度和非隶属度的转化方法,基于直觉模糊熵计算特征属性的动态权重,形成了去模糊距离测度的TOPSIS识别方法,应用于辐射源信号识别。仿真结果表明,所提方法对参数区间交叉类型的目标正确识别率较高,具有一定的实际应用价值。

     

  • 图 1  基于直觉模糊集和云模型的TOPSIS识别方法

    Figure 1.  TOPSIS recognition method based on intuitionistic fuzzy set and cloud model

    图 2  本文属性权重变化曲线

    Figure 2.  Attribute weight variation curve in this paper

    图 3  文献[10]R1类的属性权重变化曲线

    Figure 3.  Variation curve of attribute weight for R1 class in Ref.[10]

    图 4  文献[10]R2类的属性权重变化曲线

    Figure 4.  Variation curve of attribute weight for R2 class in Ref.[10]

    图 5  文献[10]R3类的属性权重变化曲线

    Figure 5.  Variation curve of attribute weight for R3 class in Ref.[10]

    图 6  文献[10]R4类的属性权重变化曲线

    Figure 6.  Variation curve of attribute weight for R4 class in Ref.[10]

    图 7  文献[10]R5类的属性权重变化曲线

    Figure 7.  Variation curve of attribute weight for R5 class in Ref.[10]

    表  1  雷达数据库

    Table  1.   Radar database

    序号 个体雷达类 射频频率
    RF/MHz
    脉冲重复周期
    RPI/μs
    脉宽
    PW/μs
    1 R1 [4 940, 5 160] [3 680, 3 750] [0.6, 1.2]
    2 R2 [5 000, 5 220] [3 630, 3 700] [0.2, 0.5]
    3 R3 [5 100, 5 420] [3 580, 3 650] [0.4, 0.7]
    4 R4 [5 400, 5 520] [3 730, 3 800] [0.6, 0.9]
    5 R5 [5 480, 5 620] [3 490, 3 600] [1, 1.4]
    6 R12 [5 000, 5 160] [3 680, 3 700]
    7 R13 [5 100, 5 160] [0.6, 0.7]
    8 R14 [3 730, 3 750] [0.6, 0.9]
    9 R15 [1, 1.2]
    10 R23 [5 100, 5 220] [3 630, 3 650] [0.4, 0.5]
    11 R34 [5 400, 5 420] [0.5, 0.7]
    12 R35 [3 580, 3 600]
    13 R45 [5 480, 5 520]
    14 R123 [5 100, 5 160]
    15 R134 [0.6, 0.7]
    下载: 导出CSV

    表  2  不同云模型的仿真结果

    Table  2.   Simulation results of different cloud models

    模型 本文 文献[10] 文献[9]
    参数1 参数2 参数3 参数1 参数2 参数3 参数1 参数2 参数3
    正确识别率/% 94.9 93.7 93.1 88 85.2 83.1 84.5 93.5 87.8
    下载: 导出CSV

    表  3  不同权重计算方法的仿真结果

    Table  3.   Simulation results of different weight calculation methods

    权重计算方法 本文 文献[10] 等权重方法
    正确识别率/% 94.2 65.4 93.5
    下载: 导出CSV

    表  4  仿真环境1的正确识别率

    Table  4.   Correct recognition rate of simulation environment 1

    获取测量值的方式 正确识别率/%
    本文方法 文献[9]方法
    R1类区间内随机抽取 93.6 86.4
    R2类区间内随机抽取 89.4 90.3
    R3类区间内随机抽取 94.2 91.5
    R4类区间内随机抽取 94.1 91.8
    R5类区间内随机抽取 99.9 97.9
    数据库区间内随机抽取 94.6 92.6
    下载: 导出CSV

    表  5  仿真环境2的正确识别率

    Table  5.   Correct recognition rate of simulation environment 2

    获取测量值的方式 本文方法正确识别率/% 文献[9]方法正确识别率/%
    情况1 情况2 情况3 情况1 情况2 情况3
    R1类区间外随机抽取 82.3 46.6 78 69.3 42.7 44.3
    R2类区间外随机抽取 69.9 54 75 78.8 62.6 55.3
    R3类区间外随机抽取 69 68.7 80.7 73.3 64.9 49.4
    R4类区间外随机抽取 74.3 73 87 63 55.6 50.7
    R5类区间外随机抽取 97.1 98 99.7 90.5 87.3 75.3
    数据库区间外随机抽取 78.8 68.5 85.3 75.8 61.5 57
    下载: 导出CSV

    表  6  方法复杂度分析

    Table  6.   Analysis of method complexity

    复杂度指标 本文 文献[10] 文献[9]
    正确识别率/% 92.6 84.5 82.6
    总耗时/s 0.482 378 0.244 235 0.438 067
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-08-16
  • 录用日期:  2019-12-15
  • 刊出日期:  2020-07-20

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