基于深度残差网络的特定协议信号识别

doi:10.3969/j.issn.0372-2112.2019.07.018

电子学报 ›› 2019, Vol. 47 ›› Issue (7): 1532-1537.DOI: 10.3969/j.issn.0372-2112.2019.07.018

基于深度残差网络的特定协议信号识别

查雄, 许漫坤, 彭华, 秦鑫, 李天昀

中国人民解放军战略支援部队信息工程大学, 河南郑州 450001

收稿日期:2018-07-21 修回日期:2018-11-07 出版日期:2019-07-25
- 作者简介:
- 查雄男,1995年出生,江西九江人.现为战略支援部队信息工程大学研究生,主要研究方向为智能信号处理.E-mail:mici0928@163.com;许漫坤 女,1977年出生,河南内黄人.现为战略支援部队信息工程大学副教授,主要研究方向为智能信号处理、图像处理与模式识别.

Specific Protocol Signal Recognition Based on Deep Residual Network

ZHA Xiong, XU Man-kun, PENG Hua, QIN Xin, LI Tian-yun

PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan 450001, China

Received:2018-07-21 Revised:2018-11-07 Online:2019-07-25 Published:2019-07-25

摘要/Abstract

摘要： 针对短波信道下信号截获质量差，信道环境复杂以及单一特征识别率低等问题，提出了基于深度残差网络的信号特征自动提取算法，设计了一种具有自适应学习能力的短波特定通信协议识别模型.通过对具有特殊结构的协议信号的时频视觉差异进行理论推导，将信号的时频能量转换成灰度图像，并用于对所构建的深度残差网络进行训练.该方法克服了传统方法对信号质量要求高、先验信息需求多等缺陷，可直接对中频接收信号进行处理，适合实际工程应用.实验表明，当深度残差网络达到稳态时，识别准确率高，在低信噪比、多径衰落、多普勒频偏以及信号被强干扰所遮挡的情况下，依旧能准确识别协议类别.

关键词: 时频分析, 深度残差网络, 低信噪比, 多径时延, 多普勒频偏, 强干扰

Abstract: To correctly classify the specific protocol signal,a signal recognition model with adaptive learning and automatic feature extraction ability is proposed.This model is based on the deep residual network,which can solve the drawbacks,such as,the poor quality of the intercepted communication signal,the complex condition of the short wave channel,and the low recognition rate of the single feature.After analyzing the visual characteristic of communication protocol signal with special structure in time frequency domain,the time frequency gray-images are obtained and utilized to train the deep residual network.This method does not need much prior knowledge and is insensitive to signal quality.Moreover,it can process the intermediate-frequency signal directly.Due to these advantages,the algorithm is suitable for engineering application.Simulation results show that,when the deep residual network reaches its steady status,the proposed model can accurately identify the protocol.And it is also proved effective even at complex circumstance where the multipath fading and the Doppler shift exist,the signal-to-noise ratio is low,and the interference is strong.

Key words: time-frequency analysis, deep residual network, low signal-to-noise ratio, multipath fading, Doppler shift, strong interference

中图分类号:

TN911.7

查雄, 许漫坤, 彭华, 秦鑫, 李天昀. 基于深度残差网络的特定协议信号识别[J]. 电子学报, 2019, 47(7): 1532-1537.

ZHA Xiong, XU Man-kun, PENG Hua, QIN Xin, LI Tian-yun. Specific Protocol Signal Recognition Based on Deep Residual Network[J]. Acta Electronica Sinica, 2019, 47(7): 1532-1537.

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基于深度残差网络的特定协议信号识别

Specific Protocol Signal Recognition Based on Deep Residual Network

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