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基于联合稀疏性的多视全极化HRRP目标识别方法

刘盛启 占荣辉 翟庆林 欧建平 张军

刘盛启, 占荣辉, 翟庆林, 欧建平, 张军. 基于联合稀疏性的多视全极化HRRP目标识别方法[J]. 电子与信息学报, 2016, 38(7): 1724-1730. doi: 10.11999/JEIT151019
引用本文: 刘盛启, 占荣辉, 翟庆林, 欧建平, 张军. 基于联合稀疏性的多视全极化HRRP目标识别方法[J]. 电子与信息学报, 2016, 38(7): 1724-1730. doi: 10.11999/JEIT151019
LIU Shengqi, ZHAN Ronghui, ZHAI Qinglin, OU Jianping, ZHANG Jun. Multi-view Polarization HRRP Target Recognition Based on Joint Sparsity[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1724-1730. doi: 10.11999/JEIT151019
Citation: LIU Shengqi, ZHAN Ronghui, ZHAI Qinglin, OU Jianping, ZHANG Jun. Multi-view Polarization HRRP Target Recognition Based on Joint Sparsity[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1724-1730. doi: 10.11999/JEIT151019

基于联合稀疏性的多视全极化HRRP目标识别方法

doi: 10.11999/JEIT151019
基金项目: 

国家自然科学基金(61471370, 61401479)

Multi-view Polarization HRRP Target Recognition Based on Joint Sparsity

Funds: 

The National Natural Science Foundation of China (61471370, 61401479)

  • 摘要: 该文考虑利用连续获取的多视全极化高分辨距离像(High Range Resolution Profile, HRRP)进行目标识别的问题。多视全极化HRRP样本包含了3个层次的先验信息:样本内各分量来自同一目标;单视内4种极化组合方式下的HRRP均对应相同的目标姿态;相同极化方式下的多视观测是相关的。为有效利用上述信息进行目标识别,该文提出一种基于联合稀疏表示的多视全极化HRRP目标识别方法。该方法约束各分量对应的稀疏表示系数共享原子级的稀疏模式。原子级稀疏约束使得从各极化字典中选择来自相同姿态的字典原子对样本中各分量进行稀疏表示,可以有效利用上述3个层次的先验信息进行目标识别。利用目标电磁散射数据对所提方法进行了验证,结果表明,该方法具有较好的识别性能,并且对噪声具有良好的鲁棒性。
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出版历程
  • 收稿日期:  2015-09-09
  • 修回日期:  2016-02-25
  • 刊出日期:  2016-07-19

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