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基于快速密度搜索聚類算法的極化HRRP分類方法

吳佳妮,  陳永光,  代大海,  陳思偉,  王雪松

吳佳妮, 陳永光, 代大海, 陳思偉, 王雪松. 基于快速密度搜索聚類算法的極化HRRP分類方法[J]. 電子與信息學報, 2016, 38(10): 2461-2467. doi: 10.11999/JEIT151457
引用本文: 吳佳妮, 陳永光, 代大海, 陳思偉, 王雪松. 基于快速密度搜索聚類算法的極化HRRP分類方法[J]. 電子與信息學報, 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類算法的極化HRRP分類方法

doi: 10.11999/JEIT151457
  • 1.

    (國防科學技術大學電子信息系統(tǒng)復雜電磁環(huán)境效應國家重點實驗室 長沙 410073) ②(北京跟蹤與通信技術研究所 北京 100094)

基金項目: 

國家自然科學基金項目(61302143, 61490693, 41301490)，國家高技術研究發(fā)展計劃(863計劃)(2013AA122202)

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

  • 1.

    (State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China)

  • 2.

    (Beijing Institute of Tracking and Telecommunications Technology, Beijing 100094, China)

Funds: 

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

  • 摘要: 該文針對人造目標的極化高分辨距離像，提出一種基于快速密度搜索聚類算法的分類方法。首先根據(jù)散射結構在頻率和極化維度的特性，對散射中心的類型進行判別，在此基礎上構造目標分類的特征矢量。然后采用快速密度搜索聚類算法，實現(xiàn)目標的分類。仿真實驗結果表明，文中構建的特征矢量能較好地描述目標的結構屬性，具有較強的可分性。而快速密度搜索聚類算法簡單高效，在人造目標的分類識別中具有極大的應用潛力。
    Abstract: A classification algorithm based on the fast density search clustering method is proposed for polarimetric High Resolution Range Profile (HRRP) of man-made target. The polarization and frequency features are used to discriminate scattering centers in order to obtain the feature vectors for target classification. After that, the fast density search clustering method is applied to classifying the man-made target. The experiments show that the feature vectors for target classification can describe the structural properties of the target and can easily be classified. The fast density search clustering method operates simply and efficiently and can be applied to the man-made target classification with excellent performance.
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出版歷程
  • 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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