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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (10): 1945-1954    DOI: 10.3785/j.issn.1008-973X.2020.10.011
    
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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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     
Received: 23 September 2019      Published: 28 October 2020
CLC:  TP 391  
Corresponding Authors: Ying GUO     E-mail: toumingwings@163.com;kdydsp@163.com
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Hong-guang LI
Ying GUO
Ping SUI
Zi-sen QI
Cite this article:

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.

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


基于时频特征的卷积神经网络跳频调制识别

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 
Fig.1 Identification process of FH modulation based on CNN
Fig.2 Time-frequency diagrams of FH signals based on four time-frequency transform methods
Fig.3 
Fig.3 Single-hop time-frequency feature map of five frequency hopping signals
Fig.4 CNN training recognition basic structure
Fig.5 Four typical CNN modulation recognition structures
Fig.6 Training results of modulation recognition rate and loss value of four CNN structures
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
Tab.1 Four CNN modulation recognition training results under different sample sets
${K_{\rm l}}$ 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
Tab.2 Training results of CNN modulation recognition based on six parameter combinations
Fig.7 Identification structure of FH modulation based on CNN
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
Tab.3 CNN modulation recognition training results with different Dropout ratios
Fig.8 FH modulation recognition rate under different SNR
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
Tab.4 Eight kinds of modulated FH signal recognition results when SNR is −4 dB
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