基于时频特征的卷积神经网络跳频调制识别  

Frequency hopping modulation recognition of convolutional neural network based on time-frequency characteristics

作　　者：李红光[1] 郭英[1] 眭萍 齐子森[1] LI Hong-guang;GUO Ying;SUI Ping;QI Zi-sen(Information and Navigation College,Air Force Engineering University,Xi’an 710077,China)

机构地区：[1]空军工程大学信息与导航学院,陕西西安710077

出　　处：《浙江大学学报（工学版）》2020年第10期1945-1954,共10页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(61601500);军队研究生资助项目(JY2018C169)。

摘　　要：针对人工设计特征表征能力不足,提取难度大的问题,提出基于卷积神经网络(CNN)的跳频信号调制方式识别系统.该系统通过训练学习跳频信号时频图特征,将调制方式识别问题转化为图像识别问题.采用组合时频变换方法对跳频信号进行时频变换得到二维时频图;经过自适应维纳滤波算法滤除背景噪声,提高系统抗噪性;采用连通域检测和双线性插值算法提取跳频信号每跳时频图,对时频图大小进行重置调整;将已处理的时频图输入到设计的11层卷积神经网络中进行训练学习,通过在输出层增加Softmax分类器,实现跳频调制方式分类识别.仿真结果表明,该系统在信噪比为–4 dB条件下,对跳频信号BPSK、QPSK、8PSK、SDPSK、QASK、16QAM、32QAM和GMSK共8种调制方式的平均识别率达到92.54%.A frequency hopping signal modulation recognition system based on convolutional neural network(CNN)was proposed aiming at the problem of insufficient artificial feature representation ability and difficulty in extraction.The modulation recognition problem was transformed into an image recognition problem by learning the timefrequency characteristics of the frequency hopping signal.The two-dimensional time-frequency diagram was obtained by using time-frequency transform,and the adaptive Wiener filter was used to remove the background noise of the time-frequency diagram to improve system noise immunity.The connected-domain detection algorithm and the bilinear interpolation algorithm were applied to extract the time-frequency diagram of each hop and reset the size.The processed time-frequency map was input into the designed 11-layer convolutional neural network for training and learning.The classification and recognition of frequency hopping modulation were realized by adding a Softmax classifier in the output layer.The simulation results showed that the average recognition rate of the frequency hopping signals BPSK,QPSK,8PSK,SDPSK,QASK,16QAM,32QAM and GMSK was 92.54%when the signalto-noise ratio was–4 dB.

关 键 词：跳频信号 调制识别 卷积神经网络(CNN) 时频变换 卷积层 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]