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基于图连通密度的海面漂浮小目标检测

时艳玲 姚婷婷 郭亚星

时艳玲, 姚婷婷, 郭亚星. 基于图连通密度的海面漂浮小目标检测[J]. 电子与信息学报, 2021, 43(11): 3185-3192. doi: 10.11999/JEIT201028
引用本文: 时艳玲, 姚婷婷, 郭亚星. 基于图连通密度的海面漂浮小目标检测[J]. 电子与信息学报, 2021, 43(11): 3185-3192. doi: 10.11999/JEIT201028
Yanling SHI, Tingting YAO, Yaxing GUO. Floating Small Target Detection Based on Graph Connected Density in Sea Surface[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3185-3192. doi: 10.11999/JEIT201028
Citation: Yanling SHI, Tingting YAO, Yaxing GUO. Floating Small Target Detection Based on Graph Connected Density in Sea Surface[J]. Journal of Electronics & Information Technology, 2021, 43(11): 3185-3192. doi: 10.11999/JEIT201028

基于图连通密度的海面漂浮小目标检测

doi: 10.11999/JEIT201028
详细信息
    作者简介:

    时艳玲:女,1983年生,副教授,研究方向为海杂波的散射特性分析、复杂电磁环境下的目标检测和雷达信号处理

    姚婷婷:女,1996年生,硕士生,研究方向为图的连通性、海面目标检测

    郭亚星:男,1997年生,硕士生,研究方向为海面目标的分类算法

    通讯作者:

    时艳玲 ylshi@njupt.edu.cn

  • 中图分类号: TN911.23

Floating Small Target Detection Based on Graph Connected Density in Sea Surface

  • 摘要: 海面漂浮小目标由于其能量弱,一直是海面目标检测的重难点。传统基于统计模型的漂浮小目标检测算法借助回波能量进行检测,没有利用数据频域幅度间的关联性,导致检测性能受损。该文借助图的处理方式,首先利用回波数据脉冲间频域幅度的关联性计算连通密度,生成邻接矩阵,接着将邻接矩阵转换为拉普拉斯矩阵,提取拉普拉斯矩阵的最大特征值作为检测特征,提出了一种基于图的连通密度的海面漂浮小目标检测算法。通过对实测的全相参的X波段 (IPIX)雷达数据进行连通密度的分析,发现海杂波构成的图比较稠密,而海面漂浮小目标构成的图比较稀疏,故通过连通密度构成的图可以有效地检测海杂波中的漂浮小目标。进一步地,通过与对比算法实验分析发现,该文所提基于图的连通密度的检测算法检测性能明显优越。
  • 图  1  图的构建及特征提取流程图

    图  2  #54 HH极化目标与杂波的幅度分布图、$n = 512$ 的频谱幅度分布图与纹理分布图

    图  3  #54 HH极化杂波单元与目标单元邻接矩阵构成的图(系数设置:$M = 10000$, $N = 512$, $P = 502$, $\gamma = 6$)

    图  4  数据#54在HV极化和VH极化下拉普拉斯矩阵最大特征值的平均值(构成邻接矩阵图的参数设置: $M = 10000$, $N = 512$, $P = 502$, $\gamma = 6$)

    图  5  数据#54和10组4种极化方式下的图特征检测器的检测性能图(参数设置:$M = 10000$, $N = 512$, $P = 502$, $\gamma = 6$)

    图  6  16组数据在4种不同极化方式下的平均信杂比

    图  7  数据#54在4种极化方式下不同观测长度和不同量化等级的检测概率($ {p}_{f} $=0.001)

    图  8  数据#54在VH极化下4种方法的检测对比(参数设置:$M = 10000$,$N = 512$,$P = 502$,$\gamma = 6$)

    表  1  IPIX雷达数据说明

    数据
    编号
    数据
    名称
    风速
    (km/h)
    浪高
    (m)
    角度
    (°)
    目标
    单元
    受影响
    单元
    1#1792.2998, 10, 11
    2#2691.19776, 8
    3#30190.99876, 8
    4#31190.99876, 8, 9
    5#4091.08875, 6, 8
    6#54200.7887, 9, 10
    7#280101.613087, 9, 10
    8#310330.93076, 8, 9
    9#311330.94076, 8, 9
    10#320280.93076, 8, 9
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-12-07
  • 修回日期:  2021-03-21
  • 网络出版日期:  2021-04-09
  • 刊出日期:  2021-11-23

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