HRRP target recognition based on kernel joint discriminant analysis

被引：0

作者：

LIU Wenbo

YUAN Jiawen

ZHANG Gong

SHEN Qian

机构：

[1] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics

[2] College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics

来源：

JournalofSystemsEngineeringandElectronics | 2019年 / 30卷 / 04期

基金：

中国国家自然科学基金;

关键词：

high resolution range profile(HRRP); target recognition; small sample problem; feature extraction; dimension reduction;

D O I：

暂无

中图分类号：

TP391.4 [模式识别与装置];

学科分类号：

0811 ; 081101 ; 081104 ; 1405 ;

摘要：

With the improvement of radar resolution, the dimension of the high resolution range profile(HRRP) has increased. In order to solve the small sample problem caused by the increase of HRRP dimension, an algorithm based on kernel joint discriminant analysis(KJDA) is proposed. Compared with the traditional feature extraction methods, KJDA possesses stronger discriminative ability in the kernel feature space. K-nearest neighbor(KNN)and kernel support vector machine(KSVM) are applied as feature classifiers to verify the classification effect. Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality, and improve target recognition performance.

引用

页码：703 / 708

页数：6