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).
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