摘要
针对雷达辐射源信号识别,提出一种基于时频分布的小波不变矩特征向量提取和识别分类方法。对雷达辐射源信号时频图像进行处理,对图像进行小波变换,提取小波矩的特征向量。采用支持向量机分类识别的方法,对特征向量进行训练,实现信号识别。对6种常见雷达信号进行分类,结果表明在信噪比较低的情况下也能取得较好的识别效果,在SNR为-3 dB时,识别正确率仍达到93.9%。
To correctly classify advanced radar emitter signal, a novel approach using the feature of wavelet invariant moment which is based on time-frequency distribution is proposed. The method processes the time-frequency image of radar emitter signal. The image is transformed by wavelet transform, and the feature vector of wavelet moment is extracted.The support vector machine classification method is used to train eigenvectors for achieving signal recognition. In this paper, six kinds of common radar signals are classified. Simulation results show that better recognition results can be obtained under the condition of low signal-to-noise ratio. When SNR is-3 dB, the average recognition rate still reaches more than 93.9%.
作者
曹晓航
汪立新
束学渊
CAO Xiaohang;WANG Lixin;SHU Xueyuan(School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China)
出处
《计算机工程与应用》
CSCD
北大核心
2020年第19期269-272,共4页
Computer Engineering and Applications
基金
国家部委预研项目。
关键词
时频分析
支持向量机
小波矩
雷达辐射源信号识别
图像识别
time-frequency analysis
support vector machine
wavelet moment
radar emitter signal identification
image identification
