基于改进CLDNN的辐射源信号识别

doi:10.3969/j.issn.1001-506X.2021.01.06

Abstract

Abstract:

Traditional methods of emitter signal recognition often need to extract features manually, which not only requires high professional knowledge, but also can not guarantee that the features selected by humans are suitable for the recognition of most types of signals, meanwhile, the recognition accuracy and speed cannot be taken into account. To solve the above problems, convolutional long short-term deep neural network(CLDNN), a deep learning model commonly used in speech processing, is introduced into the recognition of emitter signal, and the long short-term memory (LSTM) layer in this model is changed into bidirectional gated recurrent unit (Bi-GRU) layer. The input of the model is the original time series data, and the processes of feature extraction and classification recognition are carried out in the network to avoid the incompleteness of artificial feature selection. Experimental results show that the proposed model can recognize the signal types effectively at low signal to noise ratio, and a balance between recognition accuracy and recognition speed is achieved compared with other models.

Key words: emitter signal recognition, deep learning, convolutional long short-term deep neural network (CLDNN), time series

CLC Number:

TN971.1

Yicong SUN, Runlan TIAN, Xiaofeng WANG, Huixu DONG, Pu DAI. Emitter signal recognition based on improved CLDNN[J]. Systems Engineering and Electronics, 2021, 43(1): 42-47.

Figures/Tables 11

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References 21

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信号类型	参数	取值
BPSK	巴克码位数	{7, 11, 13}
Costas	频率/kHz	{[4 7 1 6 5 2 3], [2 6 3 8 7 5 1]}
Costas	采样频率/kHz	{15, 17}
FMCW	调制周期/ms	{50, 25, 35}
FMCW	调制带宽/kHz	{0.25, 0.35, 0.5}
Frank	码位数	{36, 64}
P1~P4	码位数	{36, 64}

序号	结构	损失
1	3CNN+1Bi-GRU+3DNN	0.413
2	3CNN+2Bi-GRU+3DNN	0.503
3	4CNN+1Bi-GRU+3DNN	0.432
4	2CNN+1Bi-GRU+3DNN	0.419
5	3CNN+1Bi-GRU+4DNN	0.430
6	3CNN+1Bi-GRU+2DNN	0.420

模型	总用时/s	训练轮数	平均用时/s
本文模型	3 919	86	46
RNN	1 816	100	18
LSTM	13 469	100	135
Bi-LSTM	37 016	97	382
Bi-GRU	22 536	94	240
CLDNN	3 326	100	33
AlexNet	2 254	100	23

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Emitter signal recognition based on improved CLDNN

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