最优的核判别分析用于雷达目标识别

特征提取是雷达目标识别研究中的重要问题,有效、稳健的特征是提高识别率的关键。核判别分析(KDA)是一种抽取非线性特征的有效方法,但它会因为奇异性问题而难以求解。基于子空间投影的思想,给出一种最优的核判别分析(OKDA)方法,用于对雷达目标的距离像进行特征提取,然后采用基于核的非线性分类器对所提取的特征进行分类,实现对雷达目标的识别。分别对仿真和实测距离像进行实验,结果表明该方法具有较好的识别效果。

Optimal Kernel Discriminant Analysis for Radar Target Recognition

Kernel discriminant analysis(KDA) is an effective method for nonlinear feature extraction in radar target recognition,but it is usually difficult to solve due to the singular problem. Based on the idea of subspace projection,an optimal kernel discriminant analysis(OKDA) is given and used to extract features from a range profile. Then,kernel-based nonlinear classifiers are applied for classification. Experimental results on both simulated and measured profiles show comparatively good recognition performance ...