关键词: 雷达自动目标识别, 散度比例, 线性判别分析, 特征提取, 高分辨距离像

Abstract: 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.

Key words: Radar Automatic Target Recognition (RATR), Proportion of Divergence (PD), Linear Discriminant Analysis (LDA), feature extraction, High Resolution Range Profile (HRRP)