This section demonstrates the performance of the proposed method on MSTAR database, a collection done using a 10-GHz spotlight SAR sensor with a one-foot resolution. For each target, images are captured at different depression angles over a full 0 to 359 deg range of aspect view. To ensure the accuracy and efficiency, a wide variety of experiments are performed, including configuration variations, pose and depression angle variations, outlier rejection, etc. In all the experiments, the center patch is used as the input. The cropped images are first subsampled by a factor of , and the subsampled images are then mapped into the feature space. The subsampling factor is chosen from a given interval , which corresponds to sizes , , , and . Gaussian RBF, , is employed as the kernel function, and the width parameter, , is assigned as 200, 50, 10, and 5 for the subsampled image of , , , and , respectively. The baseline methods include linear SR method12 and kernel sparse techniques, i.e., Gao et al.’s method15 [Kernel sparse representation (KSR)], Yin et al.’s method16 [Kernel sparse representation projection, (KSRP)], and Zhang et al.’s method17 [Kernel sparse representation with dimension reduction (KSRDR)], linear support vector machine (SVM) and kernel SVM.