Leakage signal classification and recognition method based on fusion features

Kou Yunfeng; Dai Fei; Zhao Zhiguo; Lü Jianming; Ma Xie

doi:10.11884/HPLPB202436.230186

Volume 36 Issue 4

Feb. 2024

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Kou Yunfeng, Dai Fei, Zhao Zhiguo, et al. Leakage signal classification and recognition method based on fusion features[J]. High Power Laser and Particle Beams, 2024, 36: 043018. doi: 10.11884/HPLPB202436.230186

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Kou Yunfeng, Dai Fei, Zhao Zhiguo, et al. Leakage signal classification and recognition method based on fusion features[J]. High Power Laser and Particle Beams, 2024, 36: 043018. doi: 10.11884/HPLPB202436.230186

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Leakage signal classification and recognition method based on fusion features

doi: 10.11884/HPLPB202436.230186

1.
Chengdu Xinxinshenfeng Electronics Co, Ltd, Chengdu 611731, China
2.
Beihang University, Beijing 100083, China
3.
China Electronics Technology Cyber Security Co., Ltd, Chengdu 610041, China

Received Date: 2023-06-19
Accepted Date: 2023-08-29
Rev Recd Date: 2023-09-21

Available Online: 2023-09-11

Publish Date: 2024-02-29

Abstract

Abstract

With the development of networks such as mobile communications, Internet of Things (IoT), V2X (meaning Vehicle to everything, including Vehicle to Vehicle and Vehicle to Infrastructure), and Industrial Internet of Things (IIoT), the electromagnetic environment is becoming increasingly complex, illegal electronic devices are also increasing day by day, and there are severe coupling and intermodulation of various signals, which bring difficulties to the identification of leaked signal types. This paper proposes a leakage signal classification and recognition method based on fused features. Comprehensively utilizing high-dimensional feature extraction methods and graphical dimensionality reduction characterization methods, and combining with deep learning models such as residual networks and feature fusion analysis methods, the method can distinguish more comprehensively multiple types of electromagnetic leakage signals. The features method has with high robustness against noise and good interpretability, and can support the intelligent detection engineering application of radiation sources based on electromagnetic signal type recognition.
- electromagnetic radiation,
- leakage signal,
- feature extraction,
- classification recognition,
- intelligent detection

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References(15)

References

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