[1]丁维雷,付永庆.kPCA特征提取算法的自动目标识别[J].应用科技,2011,38(09):32-36.[doi:10.3969/j.issn.1009-671X.2011.09.08]
　DING Weilei,FU Yongqing.Automatic target recognition based on kPCA feature extraction algorithm[J].Applied science and technology,2011,38(09):32-36.[doi:10.3969/j.issn.1009-671X.2011.09.08]
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第38卷
期数:
2011年09期
页码:
32-36
栏目:
现代电子技术
出版日期:
2011-09-05

文章信息/Info

Title:
Automatic target recognition based on kPCA feature extraction algorithm
文章编号:
1009-671X（2011）09-0032-05
作者:
丁维雷 ; 付永庆
（哈尔滨工程大学 信息与通信工程学院，黑龙江 哈尔滨 150001）
Author(s):
DING Weilei;  FU Yongqing
（College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China）
关键词:
自动目标识别; 主成分分析; 核主成分分析; 特征提取
Keywords:
automatic target recognition;  principal component analysis;  kernel principal component analysis;  feature extraction
分类号:
TN957.52
DOI:
10.3969/j.issn.1009-671X.2011.09.08
文献标志码:
A
摘要:
分析了主成分分析（PCA）与核主成分分析（kPCA）的基本原理，比较了两者在处理数据方面的性能，得出了kPCA比PCA在处理非线性可分数据方面具有优势的结论.依据几何绕射理论（GTD），通过Matlab仿真方法得到HRRP(高分辨距离像)数据，并以这些数据作为训练和测试样本，结合SVM分类方法，分别测试比较了基于4种不同核函数的分类识别性能，得出基于高斯核函数主成分分析的自动目标识别系统性能明显好于其他3种核函数的结论.
Abstract:
This paper analyzed the basic principles of PCA (principal component analysis) and kPCA (kernel principal component analysis), compared the performances of kPCA and PCA in data processing, and then drew a conclusion that kPCA has more advantages than PCA in dealing with nonlinear separable data. Based on geometrical theory of diffraction (GTD), the HRRP (high resolution range profile) data were obtained by Matlab simulation method and used as the training and test sample. And then, combining with SVM classification method, performances of automatic target recognition system based on four different kernel functions were tested respectively. The ATR (automatic target recognition) system based on principal component analysis of Gaussian kernel function was obviously better than the other three ones.

参考文献/References:

[1]王晓丹，王积勤．基于小波分解及KCN的雷达目标特征提取[J]．电波科学学报，2003，18(1)：32-37.
[2]TSUKIMOTO H. Extracting rules from neural networks[J]. Neural Networks, 2001, 11(2): 277-389.
[3]李斌，姚康泽，张银河, 等.基于HRRP统计模型的目标识别[J]. 航天电子对抗，2010，26（3）: 48-51.
[4]徐庆，王秀春，李青.基于高分辨一维像的目标特征提取方法[J]. 现代雷达, 2009, 31(6): 60-63.
[5]袁祖霞，高贵明.基于高分辨率一维距离像雷达目标识别研究[J].雷达与对抗，2010， 30(1)：11-14.
[6]POTTER L C，CHIANG D M，CARRIERE R．A GTDbased parametric model for radar scattering[J]．IEEE Trans AP，1995，43(10)：1058-l067.
[7]SCHLKOPF B, SMOLA A, MLLER K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation, 1998, 10(5): 1299-1319.
[8]SHLENS J. A tutorial on principal component analysis[J]. Computer and Information Science, 2009, 51: 52-63.
[9]STRANDMARK P. Input to kernel PCA algorithm.[2011-01-13].http://en.wikipedia.org/wiki/kernel-principle-component-analysis.
[10]廖东平.基于1类SVM的高分辨雷达真假目标识别[J]. 信号处理, 2010, 26（5）: 746-752.
[11]赵乃杰，李辉，金宝龙.基于改进高斯核函数的雷达高分辨距离像目标识别算法研究[J]. 现代电子技术， 2010，326（15）：1-4.

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备注/Memo

备注/Memo:
作者简介：丁维雷（1985-），男，硕士研究生，主要研究方向：目标检测与识别,E-mail:dingwl2011@163.com.
更新日期/Last Update: 2011-12-20