Intelligent detection algorithm of broadband communication signal based on spectral decomposition

Yi Dong; Ma Ruipeng; Hu Tao; Cheng Kaixin; Wu Di; Tian Zhifu; Wang Yanyun

doi:10.11884/HPLPB202335.230024

Volume 35 Issue 6

May 2023

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Article Navigation > High Power Laser and Particle Beams > 2023 > 35(6): 069001

Yi Dong, Ma Ruipeng, Hu Tao, et al. Intelligent detection algorithm of broadband communication signal based on spectral decomposition[J]. High Power Laser and Particle Beams, 2023, 35: 069001. doi: 10.11884/HPLPB202335.230024

Citation:

Yi Dong, Ma Ruipeng, Hu Tao, et al. Intelligent detection algorithm of broadband communication signal based on spectral decomposition[J]. High Power Laser and Particle Beams, 2023, 35: 069001. doi: 10.11884/HPLPB202335.230024

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Intelligent detection algorithm of broadband communication signal based on spectral decomposition

doi: 10.11884/HPLPB202335.230024

1.
School of Data and Target Engineering, University of Information Engineering, Zhengzhou 450001 China
2.
School of Cyberspace Security, Zhengzhou University, Zhengzhou 450002 China

Received Date: 2023-01-11
Accepted Date: 2023-03-02
Rev Recd Date: 2023-03-02

Available Online: 2023-03-08

Publish Date: 2023-05-06

Abstract

Abstract

For broadband communication signal detection problem, as the current signal detection algorithm based on deep learning is not suitable for dealing with large bandwidth and large wide broadband signals, and there is the inherent deviation in signal frequency parameter estimation, we put forward intelligent broadband communication signal detection algorithm based on spectrum decomposition, thus to complete highly accurate detection of narrow-band signal in large bandwidth receiving signal. First, the broadband signal is transformed into a grayscale time-frequency spectrum which is subsequently decomposed into a sub-spectrum suitable for the input size of the target detection network. Then, the anchor-free YOLOx target detection algorithm is used to detect the narrowband signal targets in the sub-spectrum. Finally, the signal detection results of the sub-spectrum are fused to obtain the detection results of the time-frequency parameters of the narrow-band signal. Experimental results show that the proposed algorithm can adapt to the complex noise environment. Compared with other deep learning algorithms and traditional energy detection algorithms, the proposed algorithm has higher signal detection accuracy, lower false alarm probability, smaller average error of signal parameter estimation, and stronger robustness, practicability and versatility.
- spectral decomposition,
- wide-band signal detection,
- YOLOx,
- time-frequency diagram,
- short time Fourier transform

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References

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