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

基于快速密度搜索聚类算法的极化HRRP分类方法

吴佳妮 陈永光 代大海 陈思伟 王雪松

吴佳妮, 陈永光, 代大海, 陈思伟, 王雪松. 基于快速密度搜索聚类算法的极化HRRP分类方法[J]. 电子与信息学报, 2016, 38(10): 2461-2467. doi: 10.11999/JEIT151457
引用本文: 吴佳妮, 陈永光, 代大海, 陈思伟, 王雪松. 基于快速密度搜索聚类算法的极化HRRP分类方法[J]. 电子与信息学报, 2016, 38(10): 2461-2467. doi: 10.11999/JEIT151457
WU Jiani, CHEN Yongguang, DAI Dahai, CHEN Siwei, WANG Xuesong. Target Recognition for Polarimetric HRRP Based on Fast Density Search Clustering Method[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2461-2467. doi: 10.11999/JEIT151457
Citation: WU Jiani, CHEN Yongguang, DAI Dahai, CHEN Siwei, WANG Xuesong. Target Recognition for Polarimetric HRRP Based on Fast Density Search Clustering Method[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2461-2467. doi: 10.11999/JEIT151457

基于快速密度搜索聚类算法的极化HRRP分类方法

doi: 10.11999/JEIT151457
基金项目: 

国家自然科学基金项目(61302143, 61490693, 41301490),国家高技术研究发展计划(863计划)(2013AA122202)

Target Recognition for Polarimetric HRRP Based on Fast Density Search Clustering Method

Funds: 

The National Natural Science Foundation of China (61302143, 61490693, 41301490), The National 863 Program of China (2013AA122202)

  • 摘要: 该文针对人造目标的极化高分辨距离像,提出一种基于快速密度搜索聚类算法的分类方法。首先根据散射结构在频率和极化维度的特性,对散射中心的类型进行判别,在此基础上构造目标分类的特征矢量。然后采用快速密度搜索聚类算法,实现目标的分类。仿真实验结果表明,文中构建的特征矢量能较好地描述目标的结构属性,具有较强的可分性。而快速密度搜索聚类算法简单高效,在人造目标的分类识别中具有极大的应用潜力。
  • 黄培康, 殷红成, 许小剑, 等. 雷达目标特性[M]. 北京: 电子工业出版社, 2005: 230-238.
    HUANG P K, YIN H C, XU X J, et al. Radar Target Signature[M]. Beijing: Publishing House of Electronics Industry, 2005: 230-238.
    JACKSON J A and MOSES R. Canonical scattering feature models for 3D and bistatic SAR[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(2): 525-54l. doi: 10.1109/TAES.2010.5461639.
    JACKSON J A. Three-dimensional feature models for synthetic aperture radar and experiments in feature extraction[D]. [Ph.D. dissertation], Ohio State University, 2009.
    徐牧. 极化 SAR 图像人造目标提取与几何结构反演研究[D]. [博士论文], 国防科学技术大学, 2008.
    XU M. Extraction and geometrical structure retrieval of man-made target in POLSAR imagery[D]. [Ph.D. dissertation], National University of Defense Technology, 2008.
    FULLER D F. Phase history decomposition for efficient scatterer classification in SAR imagery[D]. [Ph.D. dissertation], Air Force Institute of Technology, 2011.
    SAVILLE M A, JACKSON J A, and FULLER D F. Rethinking vehicle classification with wide-angle polarimetric SAR[J]. IEEE Aerospace Electronic Systems Magazine, 2014, 29(1): 41-49. doi: 10.1109/MAES.2014.130057.
    张瑞, 牛威, 寇鹏. 基于样本紧密度的雷达高分辨距离像识别方法研究[J]. 电子与信息学报, 2014, 36(3): 529-536. doi: 10.3724/SP.J.1146.2013.00616.
    ZHANG R, NIU W, and KOU P. Radar high resolution range profiles recognition based on the affinity[J]. Journal of Electronics Information Technology, 2014, 36(3): 529-536. doi: 10.3724/SP.J.1146.2013.00616.
    冯博, 陈渤, 王鹏辉, 等. 利用稳健字典学习的雷达高分辨距离像目标识别算法[J]. 电子与信息学报, 2015, 37(6): 1457-1462. doi: 10.11999/JEIT141227.
    FENG B, CHEN B, WANG P H, et al. Radar high resolution range profile target recognition algorithm via stable dictionary learning[J]. Journal of Electronics Information Technology, 2015, 37(6): 1457-1462. doi: 10.11999/ JEIT141227.
    WANG J, LI Y, and CHEN K. Radar high-resolution range profile recognition via geodesic weighted sparse representation[J]. IET Radar, Sonar Navigation, 2015, 9(1): 75-83. doi: 10.1049/iet-rsn.2014.0113.
    POTTER L C, CHIANG D M, CARRIERE R, et al. A GTD based parametric model for radar scattering[J]. IEEE Transactions on Antennas and Propagation, 1995, 43(10): 1058-1067. doi: 10.1109/8.467641.
    代大海. 极化雷达成像及目标特征提取研究[D]. [博士论文], 国防科学技术大学, 2008.
    DAI D H. Study on polarimetric radar imaging and target feature extraction[D]. [Ph.D. dissertation], National University of Defense Technology, 2008.
    KROGAGER E. A new decomposition of the radar target scattering matrix[J]. Electronics Letters, 1990, 26(18): 1525-1526. doi: 10.1049/el:19900979.
    LEE J S and POTTIER E. Polarimetric Radar Imaging: From Basics to Applications[M]. Boca Raton: Taylor Francis Group, 2009.
    RODRIGUEZ A and LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 334(6191): 1492-1496. doi: 10.1126/science.1242072.
    DUNGAN K E, AUSTIN C, NEHRBASS J, et al. Civilian vehicle radar data domes[J]. SPIE, 2010, 7699(5): 731-739. doi: 10.1117/12.850151.
    何护翼. 聚类算法及其应用研究[D]. [硕士论文], 上海交通大学, 2007.
    HE H Y. Study on clustering algorithm and its applications [D]. [Master dissertation], Shanghai Jiao Tong University, 2007.
  • 加载中
计量
  • 文章访问数:  1417
  • HTML全文浏览量:  133
  • PDF下载量:  460
  • 被引次数: 0
出版历程
  • 收稿日期:  2015-12-24
  • 修回日期:  2016-06-08
  • 刊出日期:  2016-10-19

目录

    /

    返回文章
    返回