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

逆高斯纹理复合高斯杂波对异常样本稳健的三分位点估计方法

水鹏朗 田超 封天

水鹏朗, 田超, 封天. 逆高斯纹理复合高斯杂波对异常样本稳健的三分位点估计方法[J]. 电子与信息学报, 2023, 45(2): 542-549. doi: 10.11999/JEIT211483
引用本文: 水鹏朗, 田超, 封天. 逆高斯纹理复合高斯杂波对异常样本稳健的三分位点估计方法[J]. 电子与信息学报, 2023, 45(2): 542-549. doi: 10.11999/JEIT211483
SHUI Penglang, TIAN Chao, FENG Tian. Outlier-robust Tri-percentile Parameter Estimation Method of Compound-Gaussian Clutter with Inverse Gaussian Textures[J]. Journal of Electronics & Information Technology, 2023, 45(2): 542-549. doi: 10.11999/JEIT211483
Citation: SHUI Penglang, TIAN Chao, FENG Tian. Outlier-robust Tri-percentile Parameter Estimation Method of Compound-Gaussian Clutter with Inverse Gaussian Textures[J]. Journal of Electronics & Information Technology, 2023, 45(2): 542-549. doi: 10.11999/JEIT211483

逆高斯纹理复合高斯杂波对异常样本稳健的三分位点估计方法

doi: 10.11999/JEIT211483
基金项目: 国家自然科学基金(62071346)
详细信息
    作者简介:

    水鹏朗:男,博士,教授,研究方向为海杂波建模与分析和雷达目标检测

    田超:男,硕士生,研究方向为海杂波统计特性分析

    封天:男,硕士生,研究方向为海杂波统计特性分析

    通讯作者:

    水鹏朗 plshui@xidian.edu.cn

  • 中图分类号: TN985.93

Outlier-robust Tri-percentile Parameter Estimation Method of Compound-Gaussian Clutter with Inverse Gaussian Textures

Funds: The National Natural Science Foundation of China (62071346)
  • 摘要: 逆高斯纹理的复合高斯分布(IG-CG分布)是描述高分辨率海杂波常用的模型,其参数估计在高分辨海用雷达自适应目标检测中起着关键作用。由于参数估计中数据不可避免地存在来自海面目标、岛礁的异常样本,对异常样本稳健的双分位点估计是近年来提出的有效方法之一。该文提出一种对异常点稳健的IG-CG分布三分位点参数估计(Tri-per)方法,其是对双分位点估计的改进。改进来自两个方面,通过双分位点位置优化提高逆形状参数的估计精度;通过第3个分位点的引入和位置优化提高尺度参数的估计精度。最后,用仿真和实测数据检验了提出估计方法的有效性和稳健性。
  • 图  1  实验选取最佳分位点组合

    图  2  第3分位点相对误差等高线图

    图  3  有异常样本条件下5种估计方法逆形状参数的估计性能对比

    图  4  IPIX数据库一组HH极化数据上5种参数估计方法的性能比较

    图  5  CSIR数据库一组VV极化数据上5种参数估计方法的性能比较

    表  1  IPIX实测数据(19980223_184853_ANTSTEP)的估计结果

    估计方法区域逆形参尺参(×100)K-S距离
    IML[12]区域A0.47263.89220.0205
    MOM24[1]纯杂波区域B0.64063.85750.0398
    MOM12[2]0.47643.85750.0238
    IML[12]0.50083.81990.0230
    BiP[12]$\beta = 0.95$0.50773.90840.0236
    Tri-per$\beta = 0.95$0.43363.66150.0269
    MOM24[1]含2%异常点的区域C11.10267.26430.2708
    MOM12[2]5.00007.26430.1388
    IML[12]1.54365.44780.0629
    BiP[12]$\beta = 0.95$0.52974.01480.0270
    Tri-per$\beta = 0.95$0.46263.70400.0269
    下载: 导出CSV

    表  2  南非CSIR实测数据(TFA10_001.01)的估计结果

    估计方法区域逆形参尺参K-S距离
    IML[12]区域A0.75760.03100.0259
    MOM24[1]纯杂波区域B1.08790.03310.0504
    MOM12[2]0.84070.03310.0296
    IML[12]0.73580.03240.0278
    BiP[12]$\beta = 0.95$0.70340.03410.0443
    Tri-per$\beta = 0.95$0.69100.03000.0343
    MOM24[1]含2%异常点的区域C2.34730.03770.1058
    MOM12[2]1.41510.03770.0454
    IML[12]1.00600.03570.0346
    BiP[12]$\beta = 0.95$0.76240.03480.0297
    Tri-per$\beta = 0.95$0.75710.03130.0287
    下载: 导出CSV
  • [1] OLLILA E, TYLER D E, KOIVUNEN V, et al. Compound-Gaussian clutter modeling with an inverse Gaussian texture distribution[J]. IEEE Signal Processing Letters, 2012, 19(12): 876–879. doi: 10.1109/LSP.2012.2221698
    [2] YU Han, SHUI Penglang, and HUANG Yuting. Low-order moment-based estimation of shape parameter of CGIG clutter model[J]. Electronics Letters, 2016, 52(18): 1561–1563. doi: 10.1049/el.2016.2248
    [3] WANG Zhihang, HE Zishu, HE Qin, et al. Adaptive CFAR detectors for mismatched signal in compound Gaussian sea clutter with inverse Gaussian texture[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 3502705. doi: 10.1109/LGRS.2020.3047390
    [4] GRIFFITHS H. Sea clutter: Scattering, the K distribution and radar performance (Ward, K. D., et al.; 2006) [book review][J]. IEEE Aerospace and Electronic Systems Magazine, 2007, 22(1): 28. doi: 10.1109/MAES.2007.327513
    [5] 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
    [6] BALLERI A, NEHORAI A, and WANG Jian. Maximum likelihood estimation for compound-gaussian clutter with inverse gamma texture[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(2): 775–779. doi: 10.1109/TAES.2007.4285370
    [7] CHALABI I and MEZACHE A. Estimators of compound Gaussian clutter with log-normal texture[J]. Remote Sensing Letters, 2019, 10(7): 709–716. doi: 10.1080/2150704X.2019.1601275
    [8] XUE Jian, XU Shuwen, and SHUI Penglang. Near-optimum coherent CFAR detection of radar targets in compound-Gaussian clutter with inverse Gaussian texture[J]. Signal Processing, 2020, 166: 107236. doi: 10.1016/j.sigpro.2019.07.029
    [9] XU Shuwen, WANG Zhexiang, BAI Xiaohui, et al. Optimum and near-optimum coherent CFAR detection of radar targets in compound-Gaussian clutter with generalized inverse Gaussian texture[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(3): 1692–1706. doi: 10.1109/TAES.2021.3120045
    [10] MEZACHE A, SOLTANI F, SAHED M, et al. Model for non-rayleigh clutter amplitudes using compound inverse Gaussian distribution: An experimental analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 142–153. doi: 10.1109/TAES.2014.130332
    [11] HUANG Penghui, ZOU Zihao, XIA Xianggen, et al. A statistical model based on modified generalized-K distribution for sea clutter[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 8015805. doi: 10.1109/LGRS.2021.3093975
    [12] SHUI Penglang, SHI Lixiang, YU Han, et al. Iterative maximum likelihood and outlier-robust bipercentile estimation of parameters of compound-Gaussian clutter with inverse Gaussian texture[J]. IEEE Signal Processing Letters, 2016, 23(11): 1572–1576. doi: 10.1109/LSP.2016.2605129
    [13] YU Han, SHUI Penglang, and LU Kai. Outlier-robust tri-percentile parameter estimation of K-distributions[J]. Signal Processing, 2021, 181: 107906. doi: 10.1016/j.sigpro.2020.107906
    [14] MIAO Yu, CHEN Yingxia, and XU Shoufang. Asymptotic properties of the deviation between order statistics and p-quantile[J]. Communications in Statistics-Theory and Methods, 2010, 40(1): 8–14. doi: 10.1080/03610920903350523
    [15] http://soma.crl.mcmast.ca/ipix.2020.
    [16] http://www.csir.co.ca/small_boat_detection.2020.
  • 加载中
图(5) / 表(2)
计量
  • 文章访问数:  446
  • HTML全文浏览量:  238
  • PDF下载量:  91
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-12-10
  • 修回日期:  2022-05-16
  • 录用日期:  2022-06-01
  • 网络出版日期:  2022-06-09
  • 刊出日期:  2023-02-07

目录

    /

    返回文章
    返回