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).
This work was partially supported by the National NSFC under grant of 60302009.
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Liu, H., Su, H., Bao, Z. (2005). Radar High Range Resolution Profiles Feature Extraction Based on Kernel PCA and Kernel ICA. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_146
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DOI: https://doi.org/10.1007/11427391_146
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
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