Radar Target Recognition Based on A Kernel Double Discriminant Subspaces Method

摘要：

Kernel Fisher discriminant analysis (KFDA) is a very effective tool used for dimensionality reduction and feature extraction in pattern recognition. However,KFDA also suffers from the so-called small sample size problem (SSS)which often existsInhigh-dimensional pattern recognition data.In this paper,we present a complete KFDA method,namely kernel double discriminant subspaces (KDDS). The new algorithm views the optimal discriminant vectors as a global transform in the feature space to some extent,and it makes full use of the discriminative information within both null and non-null subspace of the within-class scatter matrix,which makes KDDSA more powerful dicriminator. Experiments based on the measured airplanes database are conducted to evaluate the effectiveness of the proposed method,and the results show that it can obtain better classification performance.

关键词： radar target recognition range profile kernel Fisher discriminant analysis kernel double discriminant subspaces feature extraction

作者: Hualin Liu Wanlin Yang

作者单位: School of Electronic Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China

会议类型: 国际会议

会议名称: 2008 International Conference on Microwave and Millimeter Wave Technology(2008国际微波毫米波技术会议)

会议地点: 南京

会议语种:英文

页码: 1536-1539

在线出版日期: 2008-04-21（万方平台首次上网日期，不代表论文的发表时间）