Radar Signal Modulation Type Recognition Based on Denoising Convolutional Neural Network

Yihan XIAO; Liang WANG; Yuxia GUO

doi:10.11999/JEIT200506

Volume 43 Issue 8

Aug. 2021

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Yihan XIAO, Liang WANG, Yuxia GUO. Radar Signal Modulation Type Recognition Based on Denoising Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2300-2307. doi: 10.11999/JEIT200506

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Yihan XIAO, Liang WANG, Yuxia GUO. Radar Signal Modulation Type Recognition Based on Denoising Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2300-2307. doi: 10.11999/JEIT200506

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Radar Signal Modulation Type Recognition Based on Denoising Convolutional Neural Network

doi: 10.11999/JEIT200506

Yihan XIAO^{1
,
,},
Liang WANG¹,
Yuxia GUO^{2, 3}

1.
Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150000, China
2.
China Airborne Missile Academy, Luoyang 471009, China
3.
Aviation Key Laboratory of Science and Technology on Airborne Guided Weapons, Luoyang 471009, China

Funds: The National Natural Science Foundation of China (61571146), The Basic Scientific Research business Fees of the Central University (3072020CF0810), The Aviation Science Foundation (201801P6004)

Received Date: 2020-06-19
Rev Recd Date: 2021-04-10

Available Online: 2021-05-06

Publish Date: 2021-08-10

Abstract

Abstract

Considering the problems of Low Probability of Intercept (LPI) radar signal processing complexity and low recognition rate under the condition of low SNR, a signal classification and recognition system based on Denoising Convolution Neural Network (DnCNN) and Inception network is proposed. Firstly, eight kinds of LPI radar signals are transformed by Choi Williams Distribution (CWD) to obtain two-dimensional time-frequency images. Then, the denoising convolution neural network is used to denoise the time-frequency images. Finally, the images are sent to the Inception-v4 network for feature extraction, and the softmax classifier is used for classification to realize the effective classification and recognition of LPI radar signals. Simulation results show that the recognition rate of this method can still reach more than 90% under –10 dB Signal-Noise Ratio (SNR).

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References(18)

References

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