A kernel optimization method based on fusion kernel for high-resolution range profile (HRRP) is proposed in this paper. Based on the fusion of l 1 -norm and l 2 -norm Gaussian kernels, our method combines the different characteristics of them so that not only is the kernel function optimized but also the speckle fluctuations of HRRP are restrained. Then the proposed method is employed to optimize the kernel of kernel principle component analysis (KPCA) and the classification performance of extracted features is evaluated via support vector machines (SVMs) classifier. Finally, experimental results on the benchmark and radar-measured data sets are compared and analyzed to demonstrate the efficiency of our method.