针对雷达目标一维距离像识别研究, 将子空间法中的核主成分分析方法(KPCA)与LVQ神经网络相结合应用到雷达目标一维距离像识别中,提出了KPCA-LVQ算法, 并取得了较好的识别效果。研究中发现, 在使用核主成分分析时, 存在核函数中未知参数难以确定的问题。针对此问题, 深入分析核函数矩阵和核函数参数之间的关系发现,主成分的贡献率与核函数的参数之间存在着一定的对应关系。据此, 确定了基于主成分贡献率的优化问题, 并采用粒子群算法(PSO)进行优化求解, 得到最优的核参数。实验分析结果表明, 该方法克服了核主成分分析方法中依靠经验来确定未知参数的缺点, 降低了计算量, 提高了目标识别率。

The Kernel Principal Component Analysis (KPCA) method in the subspace method was used together with the LVQ neural network (KPCA-LVQ) for radar target one-dimensional range image recognition, which has achieved good recognition results. The study found that the unknown parameters in kernel function are difficult to determine when using KPCA. An in-depth analysis of the relationship between the kernel function matrix and the kernel function parameters revealed that there is a certain correspondence between the contribution rate of the principal component and the parameters of the kernel function. In this regard, an optimization problem based on principal component contribution rate was established, and Particle Swarm Optimization (PSO) algorithm was used for obtaining the optimal solution. The experimental analysis showed that the method overcomes the problem that the unknown parameters in KPCA is determined depending on experience, reduces the calculation cost and improves the target recognition rate.