[1]王 芳,盛卫星,马晓峰,等.基于B(2D)2PGNMF的ISAR像目标识别[J].南京理工大学学报(自然科学版),2013,37(06):863-868.
　Wang Fang,Sheng Weixing,Ma Xiaofeng,et al.ISAR image target recognition based on B(2D)2PGNMF[J].Journal of Nanjing University of Science and Technology,2013,37(06):863-868.
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基于B(2D)2PGNMF的ISAR像目标识别
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《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

卷:
37卷
期数:
2013年06期
页码:
863-868
栏目:
出版日期:
2013-12-31

文章信息/Info

Title:
ISAR image target recognition based on B(2D)2PGNMF
作者:
王 芳; 盛卫星; 马晓峰; 王 昊
南京理工大学 电子工程与光电技术学院,江苏 南京 210094
Author(s):
Wang Fang; Sheng Weixing; Ma Xiaofeng; Wang Hao
School of Electronic Engineering and Optoelectronic Technology,NUST,Nanjing 210094,China
关键词:
逆合成孔径雷达;  分块双向二维投影梯度非负矩阵分解;  目标识别
Keywords:
inverse synthetic aperture radar;  block two-directional and two-dimensional non-negative matrix factorization with projected gradient;  target recognition
分类号:
TN957
摘要:
为了更好地利用逆合成孔径雷达(ISAR)像目标的局部空间结构信息和类别信息实现目标识别,该文提出了一种基于分块双向二维投影梯度非负矩阵分解(B(2D)2PGNMF)的ISAR像目标识别方法。采用基向量非负加权组合的形式构建目标像。将B(2D)2PGNMF分解得到的权向量作为特征,通过最近邻分类器完成五类飞机目标的识别。仿真结果表明:在相同的压缩率或相同的基矩阵维数下,二维投影梯度非负矩阵分解(PGNMF)算法比一维PGNMF算法具有更高的识别精度,分块投影梯度非负矩阵分解(BPGNMF)算法的识别结果优于PGNMF算法,B(2D)2PGNMF算法的识别结果优于双向二维投影梯度非负矩阵分解((2D)2PGNMF)算法。在相同的基矩阵维数下,二维PGNMF算法的压缩率高于一维PGNMF算法,BPGNMF算法所需的运行时间最长,(2D)2PGNMF算法的运行时间最短。该文方法在不影响运算效率的同时能获得较好的识别结果。
Abstract:
In order to make use of local spatial structure information and classification information of inverse synthetic aperture radar(ISAR)to realize target recognition,a novel ISAR image target recognition algorithm based on block two-directional and two-dimensional non-negative matrix factorization with projected gradient(B(2D)2 PGNMF)is proposed here.Target image is constructed by the form of non-negative weighted combination of basis vectors.The weighted vectors resolved by the B(2D)2PGNMF are regarded as target features,and five-type aircraft targets are classified using a nearest neighbor classifier.The numerical results show:with the same compress ratios or dimensions of base matrix,the two-dimensional non-negative matrix factorization with projected gradient(PGNMF)has higher distinguishing accuracy than one-dimensional PGNMF; the distinguishing result of block non-negative matrix factorization with projected gradient(BPGNMF)is better than the PGNMF; the distinguishing result of the B(2D)2PGNMF is better than the two-directional and two-dimensional non-negative matrix factorization with projected gradient((2D)2PGNMF).With the same dimensions of base matrix,the compress ratio of the two-dimensional PGNMF is higher than that of the one-dimensional PGNMF,the running time of the BPGNMF is the longest and the running time of the(2D)2PGNMF is the shortest.This method has better recognition performance,and has no effect on operation efficiency.

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备注/Memo

备注/Memo:
收稿日期:2012-05-21 修回日期:2012-11-25
作者简介:王芳(1978-),女,博士生,主要研究方向:雷达目标识别,E-mail:fangzihpu@163.com; 通讯作者:盛卫星(1966-),男,教授,主要研究方向:雷达目标电磁散射特性建模及应用、阵列天线、微波成像等,E-mail:shengwx@njust.edu.cn。
引文格式:王芳,盛卫星,马晓峰,等.基于B(2D)2PGNMF的ISAR像目标识别[J].南京理工大学学报,2013,37(6):863-868.
投稿网址:http://njlgdxxb.paperonce.org
更新日期/Last Update: 2013-12-31