面向一体化应用的电磁信号智能检测方法研究  

Research on Intelligent Detection Method of Electromagnetic Signal for Integrated Application

作　　者：朱新挺 陈志坤 彭冬亮[1] Zhu Xinting;Chen Zhikun;Peng Dongliang(School of Automation,Hangzhou Dianzi University,Hangzhou,Zhejiang 310018,China)

机构地区：[1]杭州电子科技大学自动化学院,浙江杭州310018

出　　处：《信号处理》2020年第10期1708-1713,共6页Journal of Signal Processing

基　　金：国家自然科学基金资助项目(61701148);国防科技创新特区项目。

摘　　要：针对复杂电磁环境中信号检测受限于低信噪比的问题,基于信号与噪声一体化的思路,提出了一种以电磁空间的所有电磁辐射信号为背景,并结合深度学习算法的电磁信号检测方法。首先建立动态场景的电磁环境模型,包括了通信基站信号、雷达信号、干扰信号等,其次使用加高斯窗傅里叶变换提取电磁信号时频域的能量分布特征,最后采用卷积神经网络进行特征选择分类,实现信号检测。仿真结果表明,该方法在一定程度上减轻了信号检测受限于信噪比的问题,克服了传统能量检测方法和基于SVM检测方法的缺陷,提高了低信噪比下电磁信号的检测性能。For the problem that signal detection is limited by low SNR(Signal-to-Noise Ratio)in complex electromagnetic environment,based on the integration of signal and noise,with the background of all electromagnetic radiation signals in electromagnetic space and deep learning algorithm,a signal detection method is proposed.First,the electromagnetic environment model of the dynamic scene is established,including communication base station signals,radar signals,interference signals,etc.Second,the energy distribution characteristics of the electromagnetic signal in the time-frequency domain are extracted with the Gaussian window Fourier transform.Finally,the convolutional neural network is used for feature selection and classification to achieve the purpose of signal detection.The simulation results show that this method alleviates problem of signal detection limited by SNR to a certain extent,overcomes the defects of traditional energy detection methods and SVM(Support Vector Machines)-based detection methods,and improves the detection performance of electromagnetic signal under low SNR.

关 键 词：一体化 深度学习 加高斯窗傅里叶变换 信号检测 

分 类 号：TN957.51[电子电信—信号与信息处理]