Radar Emitter Identification Based on Dual Radio Frequency Fingerprint Convolutional Neural Network and Feature Fusion

XIAO Yihan; WANG Boyu; YU Xiangzhen; JIANG Yilin

doi:10.11999/JEIT231236

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XIAO Yihan, WANG Boyu, YU Xiangzhen, JIANG Yilin. Radar Emitter Identification Based on Dual Radio Frequency Fingerprint Convolutional Neural Network and Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231236

Citation:

XIAO Yihan, WANG Boyu, YU Xiangzhen, JIANG Yilin. Radar Emitter Identification Based on Dual Radio Frequency Fingerprint Convolutional Neural Network and Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231236

Citation:

XIAO Yihan, WANG Boyu, YU Xiangzhen, JIANG Yilin. Radar Emitter Identification Based on Dual Radio Frequency Fingerprint Convolutional Neural Network and Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231236

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Radar Emitter Identification Based on Dual Radio Frequency Fingerprint Convolutional Neural Network and Feature Fusion

doi: 10.11999/JEIT231236

1.
Key Laboratory of Advanced Marine Communication and Information Technology, Harbin Engineering University, Harbin 150000, China
2.
Shanghai Radio Equipment Research Institute, Shanghai 201100, China

Received Date: 2023-11-07
Available Online: 2024-04-25

Abstract

Abstract

In order to achieve identification of radar emitter unaffected by signal parameters and modulation methods, a method based on Dual Radio Frequency Fingerprint Convolutional Neural Network (Dual RFF-CNN2) and feature fusion is proposed in this paper. Firstly, Raw-I/Q signals are extracted from the received radio frequency signals. Secondly, Axially Integral Bispectrum (AIB) and Square Integral Bispectrum (SIB) dimensionality reduction are performed separately on Raw-In-phase/Quadrature (Raw-I/Q) signals to construct the bispectrum integration matrix. Finally, both the Raw-I/Q signals and the bispectrum integration matrix are fed into the Dual RFF-CNN2 network for feature fusion to achieve identification of radar emitter. Experimental results demonstrate that this method achieves high identification accuracy, and the extracted "fingerprint features" exhibit stability and robustness.

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References(22)

References

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