中文摘要：

本文提出一种针对雷达一维高分辨距离像（HRRP）的核函数优化算法．该算法基于对模-1距离高斯核和模-2距离高斯核的融合，结合两种核函数的不同特性，不仅优化了核函数，同时抑制了HRRP的闪烁效应．文中，基于雷达实测数据，我们将所提算法应用于核主分量分析（KPCA）的核函数优化中，然后采用支持矢量机（SVM）对提取的特征进行了分类．通过对实验结果比较与分析，我们证明该方法是有效的．

英文摘要：

A kernel optimization based on fusion kernel for HRRP is proposed. Based on the fusion of the 1-norm and 2-norm Gaussian kernels, our method combines the different characteristics of them so that not only the kernel function is optimized but also the speckle fluctuations of HRRP are restrained. Then the presented method is employed to the kernel optimization of KPCA and the classification performance of the extracted is evaluated via a SVM classifier. Finally, experiment results on the radar measured data are compared and analyzed, which prove our method effective.