首页期刊简介编委会投稿启事审稿流程读者订阅广告服务联系我们English
引用本文
  •    [点击复制]
  •    [点击复制]
【打印本页】 【下载PDF全文】 查看/发表评论下载PDF阅读器关闭

←前一篇|后一篇→

过刊浏览    高级检索

本文已被:浏览 168次   下载 0 本文二维码信息
码上扫一扫!
基于对角切片特征提取和深度学习的辐射源识别
李楠
0
(西京学院 信息工程学院,西安 710123)
摘要:
针对复杂电磁环境下辐射源识别率低的问题,提出基于对角切片特征和深度学习的辐射源识别算法。利用辐射源信号双谱的个体特性,提取信号双谱对角切片特征作为深度学习模型的输入数据,采用Softmax分类器进行辐射源识别。仿真实验利用两部同型辐射源进行测试,结果表明该算法能识别个体辐射源,在低信噪比条件下也能获得高的辐射源识别率;相比于其他识别算法,双谱对角切片特征有更鲁棒的分辨性。
关键词:  辐射源识别  复杂电磁环境  双谱  对角切片  深度学习
DOI:
基金项目:
Emitter recognition based on diagonal slice feature and deep learning
LI Nan
(School of Information Engineering,Xijing University,Xi′an 710123,China)
Abstract:
For the problem of low recognition rate of emitter in complex electromagnetic environment,an algorithm based on diagonal slice feature and deep learning is proposed.The algorithm takes advantage of the individual characteristics of the bi-spectrum of the emitter signal,extracts the signal bi-spectrum diagonal slice feature as the input data of the deep learning model,and uses the Softmax classifier to recognize the emitter.The simulation results show that the algorithm can recognize the individual emitter and obtain high recognition rate of emitter under low signal-to-noise ratio,compared with those of other recognition algorithms,bi-spectral diagonal slice features have more robust resolution.
Key words:  emitter recognition  complex electromagnetic environment  bi-spectrum  diagonal slice  deep learning
安全联盟站长平台