基于散度比例准则的高分辨距离像特征提取

针对传统线性判别分析(LDA)的子空间倾向于保留大类间距离类对的可分性，而丢弃小类间距离类对的可分性的问题，基于子空间应均衡保留各类对可分性的思想，提出一种新的准则——散度比例(PD)准则。PD准则为各类对子空间散度与原空间散度之比的均值，并推导出最大化PD准则的线性判别分析(PD-LDA)的求解过程。采用PD-LDA对高分辨距离像(HRRP)的幅度谱进行特征提取，基于外场实测数据，分别训练了最小欧氏距离分类器和支持向量机(SVM)分类器，两种分类器的识别结果均表明，PD-LDA相比LDA，可显著降低数据维数并有效提高识别率。

HRRP feature extraction based on proportion of divergence criterion

Traditional Linear Discriminant Analysis(LDA) faces the problem of tending to keep the separability of the class pairs having large within-class distances,while discarding the separability of those having small within-class distances.Based on the viewpoint that the feature subspace should uniformly keep the separability of each class pair,a new criterion,i.e.,the Proportion of Divergence(PD),was presented.PD criterion was the mean of the proportion of the subspace divergence to original space divergence of each class pair.The solution of the Linear Discriminant Analysis(LDA) maximizing PD criterion(PD-LDA) was also presented.PD-LDA was used to perform feature extraction in the amplitude spectrum space of High Resolution Range Profile(HRRP).Shortest Euclidian distance classifier and Support Vector Machine(SVM) classifier were designed to evaluate the recognition performance.The experimental results for measured data show that,compared with traditional LDA,PD-LDA reduces data dimension remarkably and improves recognition rate effectively.