Radar high range resolution profiles feature extraction based on kernel PCA and kernel ICA

Authors:
Hongwei Liu;Hongtao Su;Zheng Bao
Affiliations:
National Lab of Radar Signal Processing, Xidian University, Xi'an, Shaanxi, China;National Lab of Radar Signal Processing, Xidian University, Xi'an, Shaanxi, China;National Lab of Radar Signal Processing, Xidian University, Xi'an, Shaanxi, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 3
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Abstract

Kernel based nonlinear feature extraction approaches, kernel principal component analysis (KPCA) and kernel independent component analysis (KICA), are used for radar high range resolution profiles (HRRP) feature extraction. The time-shift uncertainty of HRRP is handled by a correlation kernel function, and the kernel basis vectors are chosen via a modified LBG algorithm. The classification performance of support vector machine (SVM) classifier based on KPCA and KICA features for measured data are evaluated, which shows that the KPCA and KICA based feature extraction approaches can achieve better classification performance and are more robust to noise as well, comparing with the adaptive Gaussian classifier (AGC).