PCA and kernel PCA for radar high range resolution profiles recognition

Radar high range resolution profile (HRRP) contains target structure information. It is shown to be a promising signature for radar automatic target recognition. As a method for data dimension reduction and feature extraction, principle component analysis (PCA) and kernel PCA have found wide applications in pattern recognition field. According to the characteristics of target pose sensitivity and shift sensitivity, a localized PCA and a modified KPCA are proposed for radar HRRP recognition. Also the methods for selecting the kernel basis vectors and handling the range-shift alignment are carefully addressed. Finally, support vector machine (SVM) classifier is used to evaluate the classification performance based on measured data. Experimental results show the proposed methods are effective and KPCA outperforms PCA.