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基于核主分量相关判别分析特征提取方法的目标HRRP识别

李龙 刘峥

冯维, 冯穗力, 丁跃华, 黄鑫. 无线多跳网络下基于过时信道状态信息的跨层资源分配[J]. 电子与信息学报, 2014, 36(11): 2750-2755. doi: 10.3724/SP.J.1146.2013.00546
引用本文: 李龙, 刘峥. 基于核主分量相关判别分析特征提取方法的目标HRRP识别[J]. 电子与信息学报, 2018, 40(1): 173-180. doi: 10.11999/JEIT170329
Feng Wei, Feng Sui-Li, Ding Yue-Hua, Huang Xin. Cross-layer Resource Allocation with Outdated Channel State Information in Wireless Multi-hop Networks[J]. Journal of Electronics & Information Technology, 2014, 36(11): 2750-2755. doi: 10.3724/SP.J.1146.2013.00546
Citation: LI Long, LIU Zheng. Kernel Principal Component Correlation and Discrimination Analysis Feature Extraction Method for Target HRRP Recognition[J]. Journal of Electronics & Information Technology, 2018, 40(1): 173-180. doi: 10.11999/JEIT170329

基于核主分量相关判别分析特征提取方法的目标HRRP识别

doi: 10.11999/JEIT170329

Kernel Principal Component Correlation and Discrimination Analysis Feature Extraction Method for Target HRRP Recognition

  • 摘要: 为有效提高雷达高分辨1维距离像目标识别系统的总体性能,需要对目标高分辨1维距离像进行特征提取,以得到具有最小信息损失、高可分性且低维度的目标特征,为实现该目的提出一种基于核主分量相关判别分析的特征提取算法。该算法基于目标高分辨1维距离像的统计特性,通过对核主分量分析中核函数的选择,实现对不同类型距离单元的特征提取。同时综合线性判别分析与典型相关分析理论构建新的准则函数,以实现特征空间中类内相关性与类间差异性最大化,同时减少目标特征中的冗余信息。利用实测数据进行实验,结果表明该方法提高了特征向量的可分性,降低了特征向量的维度,并且对该算法在不同强度杂波下的识别性能进行了分析,实验结果表明,该方法可以有效的提高目标高分辨1维距离像目标识别系统的总体性能。
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    DU Lan, HE Hua, ZHAO Le, et al. Noise robust radar HRRP target recognition based on scatterer matching algorithm[J]. IEEE Sensors Journal, 2016, 16(6): 1743-1753. doi: 10.1109/ JSEN.2015.2501850.
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    其他类型引用(6)

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出版历程
  • 收稿日期:  2017-04-14
  • 修回日期:  2017-07-10
  • 刊出日期:  2018-01-19

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