Radar radiation source recognition method based on compressed residual network

Guo Enze; Liu Zhengtang; Cui Bo; Liu Guobin; Shi Hangyu; Jiang Xu

doi:10.11884/HPLPB202436.230119

Volume 36 Issue 4

Feb. 2024

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Guo Enze, Liu Zhengtang, Cui Bo, et al. Radar radiation source recognition method based on compressed residual network[J]. High Power Laser and Particle Beams, 2024, 36: 043016. doi: 10.11884/HPLPB202436.230119

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Guo Enze, Liu Zhengtang, Cui Bo, et al. Radar radiation source recognition method based on compressed residual network[J]. High Power Laser and Particle Beams, 2024, 36: 043016. doi: 10.11884/HPLPB202436.230119

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Radar radiation source recognition method based on compressed residual network

doi: 10.11884/HPLPB202436.230119

1.
Unit 63893 of the PLA, Luoyang 471003, China
2.
Unit 63896 of the PLA, Luoyang 471003, China

Received Date: 2023-05-06
Accepted Date: 2023-10-20
Rev Recd Date: 2023-10-20

Available Online: 2023-10-28

Publish Date: 2024-02-29

Abstract

Abstract

Aiming at the problems of low recognition accuracy and poor timeliness of existing radar emitter signal recognition methods under the condition of low SNR, this paper proposes a radar emitter signal recognition method based on compressed residual network. Using Choi-Williams distribution for reference, the time-domain signal is converted into a two-dimensional time-frequency image, which improves the effectiveness of signal essential feature extraction. According to the characteristics of the application scenario, it selects the “compression” range of convolutional neural networks (CNN), and builds a compression residual network to automatically extract image features and identify. The simulation results show that compared with other advanced models, the proposed method can reduce the running time of signal recognition by about 88%, and the average recognition rate of 14 radar emitter signals is at least 5% higher when the signal-to-noise ratio is −14 dB. This paper provides an efficient intelligent recognition method of radar emitter signal, which has potential engineering application prospects.
- compressed residual network,
- time-frequency analysis,
- radar radiation source recognition,
- deep learning,
- dilateded convolution

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References(30)

References

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