摘要
局部线性嵌入(LLE)是一种有代表性的流形学习算法,利用核技术将LLE进行推广,得到核局部线性嵌入算法,并将其应用于雷达目标一维距离像的特征提取。然后采用一种基于核的非线性分类器,对所提取的特征进行分类。对3种飞机的实测数据进行识别实验,结果表明,该方法具有较优的识别性能。
Locally linear embedding(LLE)is one of the representative manifold learning algorithms.In this paper,LLE is extended using kernel technique,which leads to kernel locally linear embedding(KLLE)algorithm.KLLE is first utilized to ex- tract nonlinear features from a range profile.Then,a kernel-based nonlinear classifier,called KNR(Kernel-based Nonlinear Repr- esentor),is introduced and employed for classification.Experimental results on measured profiles from three aircrafts show rela- tively good recognition ...
出处
《现代雷达》
CSCD
北大核心
2008年第10期39-42,共4页
Modern Radar
基金
教育部重点基金项目(No.105150)
ATR重点实验室基金项目(No.51483010305DZ0207)