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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (10): 1945-1954    DOI: 10.3785/j.issn.1008-973X.2020.10.011
计算机技术     
基于时频特征的卷积神经网络跳频调制识别
李红光(),郭英*(),眭萍,齐子森
空军工程大学 信息与导航学院，陕西 西安 710077
Frequency hopping modulation recognition of convolutional neural network based on time-frequency characteristics
Hong-guang LI(),Ying GUO*(),Ping SUI,Zi-sen QI
Information and Navigation College, Air Force Engineering University, Xi’an 710077, China
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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%.
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    
Abstract:

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 time-frequency 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 signal-to-noise ratio was –4 dB.
Key words: frequency hopping signal    modulation recognition    convolutional neural network (CNN)    time-frequency transform    convolution layer
收稿日期: 2019-09-23 出版日期: 2020-10-28
CLC:  TP 391  
基金资助: 国家自然科学基金资助项目(61601500)；军队研究生资助项目(JY2018C169)
通讯作者: 郭英     E-mail: toumingwings@163.com;kdydsp@163.com
作者简介: 李红光（1986—），男，博士，从事信息对抗理论的研究. orcid.org/0000-0002-3466-4396. E-mail： toumingwings@163.com
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李红光
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引用本文:

李红光,郭英,眭萍,齐子森. 基于时频特征的卷积神经网络跳频调制识别[J]. 浙江大学学报(工学版), 2020, 54(10): 1945-1954.

Hong-guang LI,Ying GUO,Ping SUI,Zi-sen QI. Frequency hopping modulation recognition of convolutional neural network based on time-frequency characteristics. Journal of ZheJiang University (Engineering Science), 2020, 54(10): 1945-1954.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.10.011    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I10/1945

图 1  基于CNN的跳频调制方式识别流程
图 2  4种时频变换方法的FH信号时频图
图 3  
图 3  5种跳频调制信号单跳时域和时频域特征图
图 4  CNN训练识别基本结构
图 5  4种典型的CNN调制识别结构
图 6  4种CNN结构的调制识别率和损失值的训练结果
CNN结构 训练集 验证集 测试集
Rpsr / % Lloss Rpsr / % Lloss Rpsr / % Lloss
CNN_1 100 0.0076 80.05 0.0892 79.59 0.0928
CNN_2 100 0.0045 81.92 0.0812 81.21 0.0873
CNN_3 100 0.0027 85.06 0.0553 84.83 0.0576
CNN_4 100 0.0032 84.69 0.0574 84.14 0.0603
表 1  不同样本集下的4种CNN调制识别训练结果
${K_{\rm l}}$ r ${R_{\rm{c}}}$/kB $V$/（幅·s?1） ${R_{{\rm{psr}}}}$/%
$7 \times 7$ 2 2269.476 2.92 91.32
$5 \times 5$ 2 1585.653 5.24 87.39
$3 \times 3$ 2 357.248 8.23 83.46
$3 \times 3$ 4 357.248 8.23 86.75
$3 \times 3$ 6 357.248 8.23 90.14
$3 \times 3$ 8 357.248 8.23 91.18
表 2  6种参数组合的CNN调制识别训练结果
图 7  基于CNN的跳频调制方式识别结构
p/% 训练集 验证集 测试集
Rpsr / % Lloss Rpsr / % Lloss Rpsr / % Lloss
0 100 0.006 2 91.88 0.085 3 90.05 0.087 4
10 100 0.013 7 87.92 0.184 7 87.42 0.188 5
20 100 0.006 9 89.24 0.093 8 88.21 0.100 4
30 100 0.007 5 88.13 0.096 6 87.98 0.099 5
40 99.93 0.062 1 90.75 0.088 2 90.62 0.110 6
50 100 0.003 8 92.81 0.087 1 92.63 0.095 4
60 100 0.004 3 89.46 0.109 2 88.34 0.101 3
70 99.96 0.076 7 88.25 0.114 7 87.83 0.132 8
80 100 0.006 6 91.33 0.087 1 91.35 0.099 6
90 100 0.009 2 89.75 0.093 6 89.43 0.101 2
表 3  不同Dropout比例的CNN调制识别训练结果
图 8  不同信噪比下的FH调制识别率
输出 Rpsr /%
输入BPSK 输入QPSK 输入SDPSK 输入8PSK 输入QASK 输入16QAM 输入32QAM 输入GMSK
BPSK 89.2 1.3 6.8 1.8 0 1.6 0 1.5
QPSK 2.1 94.2 1.3 3.1 0.5 0 1.8 1.3
SDPSK 8.1 1.4 91.1 1.7 0 0 0 2.1
8PSK 0.6 2.3 0.8 92.8 0 0 0 0.5
QASK 0 0.8 0 0.6 94.6 2.5 1.4 0
16QAM 0 0 0 0 3.2 91.7 4.7 0
32QAM 0 0 0 0 1.7 4.2 92.1 0
GMSK 0 0 0 0 0 0 0 94.6
表 4  信噪比为−4 dB时8种FH信号调制方式的识别结果
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