Radar emitter signal recognition based on feature selection algorithm

被引：0

作者：

Zhang, GX ^{[1
]}

Hu, LZ

Jin, WD

机构：

[1] Natl EW Lab, Sichuan 610036, Peoples R China

[2] SW Jiaotong Univ, Sch Elect Engn, Chengdu 610031, Sichuan, Peoples R China

来源：

AI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2004年 / 3339卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Rough set theory (RST) was introduced into radar emitter signal (RES) recognition. A novel approach was proposed to discretize continuous interval valued features and attribute reduction method was used to select the best feature subset from original feature set. Also, rough neural network (NN) classifier was designed. Experimental results show that the proposed hybrid approach based on RST and NN achieves very high recognition rate and good efficiency. It is proved to be a valid and practical approach.

引用

页码：1108 / 1114

页数：7

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