高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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
  • [1] 丁昊, 刘宁波, 董云龙, 等. 雷达海杂波测量试验回顾与展望[J]. 雷达学报, 2019, 8(3): 281–302. doi: 10.12000/JR19006

    DING Hao, LIU Ningbo, DONG Yunlong, et al. Overview and prospects of radar sea clutter measurement experiments[J]. Journal of Radars, 2019, 8(3): 281–302. doi: 10.12000/JR19006
    [2] 张坤, 水鹏朗, 王光辉. 相参雷达K分布海杂波背景下非相干积累恒虚警检测方法[J]. 电子与信息学报, 2020, 42(7): 1627–1635. doi: 10.11999/JEIT190441

    ZHANG Kun, SHUI Penglang, and WANG Guanghui. Non-coherent integration constant false alarm rate detectors against K-distributed sea clutter for coherent radar systems[J]. Journal of Electronics &Information Technology, 2020, 42(7): 1627–1635. doi: 10.11999/JEIT190441
    [3] ZHOU Wei, XIE Junhao, LI Gaopeng, et al. Robust CFAR detector with weighted amplitude iteration in nonhomogeneous sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(3): 1520–1535. doi: 10.1109/TAES.2017.2671798
    [4] SHI Yanling. Three GLRT detectors for range distributed target in grouped partially homogeneous radar environment[J]. Signal Processing, 2017, 135: 121–131. doi: 10.1016/j.sigpro.2016.12.030
    [5] ROBEY F C, FUHRMANN D R, KELLY E J, et al. A CFAR adaptive matched filter detector[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 208–216. doi: 10.1109/7.135446
    [6] LI Dongchen and SHUI Penglang. Floating small target detection in sea clutter via normalised Hurst exponent[J]. Electronics Letters, 2014, 50(17): 1240–1242. doi: 10.1049/el.2014.1569
    [7] SHI Yanling, ZHANG Xueliang, and LIU Zipeng. Floating small target detection in sea clutter based on jointed features in FRFT domain[C]. The 3rd EAI International Conference on Advanced Hybrid Information Processing, Nanjing, China, 2019: 128–139. doi: 10.1007/978-3-030-36405-2_14.
    [8] 陈小龙, 关键, 于晓涵, 等. 基于短时稀疏时频分布的雷达目标微动特征提取及检测方法[J]. 电子与信息学报, 2017, 39(5): 1017–1023. doi: 10.11999/JEIT161040

    CHEN Xiaolong, GUAN Jian, YU Xiaohan, et al. Radar Micro-Doppler signature extraction and detection via short-time sparse time-frequency distribution[J]. Journal of Electronics &Information Technology, 2017, 39(5): 1017–1023. doi: 10.11999/JEIT161040
    [9] SHI Yanling, XIE Xiaoyan, and LI Dongchen. Range distributed floating target detection in sea clutter via feature-based detector[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1847–1850. doi: 10.1109/LGRS.2016.2614750
    [10] 时艳玲, 杜宇翔, 蒋锐, 等. 部分均匀海杂波中基于分组加权的协方差矩阵估计算法[J]. 信号处理, 2019, 35(7): 1170–1179. doi: 10.16798/j.issn.1003-0530.2019.07.006

    SHI Yanling, DU Yuxiang, JIANG Rui, et al. A grouped weighted covariance matrix estimator in partially homogeneous sea clutter[J]. Journal of Signal Processing, 2019, 35(7): 1170–1179. doi: 10.16798/j.issn.1003-0530.2019.07.006
    [11] XU Shuwen, ZHENG Jibin, PU Jia, et al. Sea-surface floating small target detection based on polarization features[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(10): 1505–1509. doi: 10.1109/LGRS.2018.2852560
    [12] 陈世超, 高鹤婷, 罗丰. 基于极化联合特征的海面目标检测方法[J]. 雷达学报, 2020, 9(4): 664–673. doi: 10.12000/JR20072

    CHEN Shichao, GAO Heting, and LUO Feng. Target detection in sea clutter based on combined characteristics of polarization[J]. Journal of Radars, 2020, 9(4): 664–673. doi: 10.12000/JR20072
    [13] CHEN Shichao, LUO Feng, and LUO Xianxian. Multiview feature-based sea surface small target detection in short observation time[J]. IEEE Geoscience and Remote Sensing Letters, 2020(99): 1–5. doi: 10.1109/LGRS.2020.2994341
    [14] SANDRYHAILA A and MOURA J M F. Discrete signal processing on graphs: Frequency analysis[J]. IEEE Transactions on Signal Processing, 2014, 62(12): 3042–3054. doi: 10.1109/tsp.2014.2321121
    [15] 姜琦, 王锐, 周超, 等. 基于代数图论的修正贝叶斯群目标航迹起始算法[J]. 电子与信息学报, 2021, 43(3): 531–538. doi: 10.11999/JEIT200449

    JIANG Qi, WANG Rui, ZHOU Chao, et al. Modified Bayesian group target track initiation algorithm based on algebraic graph theory[J]. Journal of Electronics &Information Technology, 2021, 43(3): 531–538. doi: 10.11999/JEIT200449
    [16] YAN Kun, WU H C, XIAO Hailin, et al. Novel robust band-limited signal detection approach using graphs[J]. IEEE Communications Letter, 2017, 21(1): 20–23. doi: 10.1109/LCOMM.2016.2618871
    [17] YAN Kun, BAI Yu, WU H C, et al. Robust target detection within sea clutter based on graphs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 7093–7103. doi: 10.1109/TGRS.2019.2911451
    [18] 李炯生, 张晓东, 潘永亮. 图的Laplace特征值[J]. 数学进展, 2003, 32(2): 157–165. doi: 10.3969/j.issn.1000-0917.2003.02.003

    LI Jiongsheng, ZHANG Xiaodong, and PAN Yongliang. Laplacian eigenvalues of graphs[J]. Advances in Mathematics, 2003, 32(2): 157–165. doi: 10.3969/j.issn.1000-0917.2003.02.003
    [19] DE ABREU N M M. Old and new results on algebraic connectivity of graphs[J]. Linear Algebra and its Applications, 2007, 423(1): 53–73. doi: 10.1016/j.laa.2006.08.017
    [20] LI Ying, YANG Yonghu, and ZHU Xueyuan. Target detection in sea clutter based on multifractal characteristics after empirical mode decomposition[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(9): 1547–1551. doi: 10.1109/LGRS.2017.2721463
    [21] HAYKIN S. The mcmaster IPIX radar sea clutter database in 1993[EB/OL]. http://soma.ece.mcmaster.ca/ipix/dartmouth, 2016.
    [22] SHUI Penglang, LI Dongchen, and XU Shuwen. Tri-feature-based detection of floating small targets in sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1416–1430. doi: 10.1109/TAES.2014.120657
  • 加载中
图(8) / 表(1)
计量
  • 文章访问数:  1152
  • HTML全文浏览量:  539
  • PDF下载量:  159
  • 被引次数: 0
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

目录

    /

    返回文章
    返回