金炜东,,
葛鹏,
李冰
西南交通大学电气工程学院? ?成都? ?610031
基金项目:国家重点研发计划项目(2016YFB1200401-102F)，中央高校基本科研业务费专项资金(2682017CX046)
详细信息
作者简介:黄颖坤：男，1989年生，博士生，研究方向为雷达信号处理，机器学习
金炜东：男，1959年生，教授，博士生导师，研究方向为智能信息处理、系统仿真与优化方法
葛鹏：男，1986年生，讲师，研究方向为雷达信号处理，电子对抗
李冰：女，1988年生，讲师，研究方向为电磁场与电磁波，微波成像
通讯作者:金炜东 wdjin@home.swjtu.edu.cn
中图分类号:TN95计量
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被引次数:0
出版历程
收稿日期:2018-05-30
修回日期:2019-02-25
网络出版日期:2019-03-04
刊出日期:2019-05-01
Radar Emitter Signal Identification Based on Multi-scale Information Entropy
Yingkun HUANG,Weidong JIN,,
Peng GE,
Bing LI
College of Electrical Engineering, Southwest Jiao Tong University, Chengdu 610031, China
Funds:The National Key Research and Development Program (2016YFB1200401-102F), The Fundamental Research Funds for the Central Universities (2682017CX046)
摘要
摘要:随着雷达信号的日益复杂，从实数序列中提取特征变得越来越困难，但当它们表示成符号序列时，通常能更容易地挖掘出有效的特征参数。因此，该文提出一种基于多尺度信息熵(MSIE)的雷达信号识别方法。首先通过符号聚合近似(SAX)算法在不同字符集尺度下将雷达信号转换为符号化序列；然后联合各符号序列的信息熵值，组成MSIE特征向量；最后，使用k邻近算法(k-NN)作为分类器实现雷达信号的分类识别。通过仿真6种典型的雷达信号进行验证，结果表明该方法在信噪比(SNR)为5 dB时，不同雷达信号的识别正确率大于90%，并且优于传统的基于复杂度特征(盒维数和稀疏性)的识别方法。
关键词:雷达信号识别/
符号聚合近似算法/
多尺度信息熵/
k邻近算法
Abstract:With the increasing complexity of radar signals, it is more and more difficult to extract features of the real sequences, but when they are transformed to a symbol sequence, it is usually easier to mine the effective feature parameters. Therefore, a radar signal recognition method based on Multi-Scale Information Entropy (MSIE) is proposed. Firstly, the radar signal is transformed into symbolic sequence by Symbolic Aggregate approXimation (SAX) algorithm under different character number scales. Then, the information entropy of each symbol sequence is combined to form the MSIE feature vector. Finally, the k-Nearest Neighbor (k-NN) is used as a classifier to realize the classification and identification of radar signals. The simulation results of 6 typical radar signals show that using the proposed method the correct recognition rate of different radar signals is greater than 90% when Signal to Noise Ratio (SNR) is 5 dB, and better performance can be obtaned conpared with the traditional identification method based on complexity characteristics (box-dimension and sparseness).
Key words:Radar signal identification/
Symbolic Aggregate approXimation (SAX) algorithm/
Multi-Scale Information Entropy (MSIE)/
k-Nearest Neighbor (k-NN) algorithm
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