Recognition of Radar Target Based on Kernel Projection Subspace using Range Profiles

Daiying Zhou
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Abstract

A novel subspace method of radar target recognition, which is named as kernel projection subspace (KPS) method, is proposed in this paper, in which geometric relations are preserved according to prior class-label information and nonlinear variations of range profiles are represented by nonlinear mapping. So, the KPS can be used to extract target feature for enhancing local within-class relations. The experimental results of four kinds of planes demonstrate the efficiency of approach proposed in this paper.
基于距离像核投影子空间的雷达目标识别
提出了一种新的雷达目标识别子空间方法——核投影子空间(KPS)方法,该方法根据先验类标记信息保持几何关系,并用非线性映射表示距离像的非线性变化。因此,KPS可以用于提取目标特征,以增强局部类内关系。四种平面的实验结果证明了本文方法的有效性。
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