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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。
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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

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