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

LI Long; LIU Zheng

doi:10.11999/JEIT170329

Volume 40 Issue 1

Jan. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(1): 173-180

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

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

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

doi: 10.11999/JEIT170329

LI Long¹,
LIU Zheng¹

Received Date: 2017-04-14
Rev Recd Date: 2017-07-10
Publish Date: 2018-01-19

Abstract

Abstract

For radar High Resolution Range Profile (HRRP) automatic target recognition, the features should be extracted with sufficient target information, high discrimination, noise robustness, and low feature vector dimension. However, radar HRRP recognition suffers from insufficient amount of information and low discrimination feature, besides the radar recognition system also need the ability of real-time processing with low dimension. To obtain features with merits of low-dimension and high-discrimination, a novel feature extraction method is designed for radar high range resolution profile, namely Kernel Principal Component Correlation and Discrimination Analysis (KPCCDA). With the proposed method, the statistical characteristics of different scatter range cells can be effectively used by Kernel Principal Component Analysis (KPCA). And the within-class correlation and between- class discrimination are maximized with linear discrimination analysis and canonical correlation analysis used. Besides, the redundancy and dimensionality of the feature vectors are reduced, yielding a lowered computational complexity to meet the storage requirement in practical radar target recognition. Experimental results with measured data validate the efficiency of the proposed method.

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References(20)

References

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