Radar Emitter Identification Based on Dual Radio Frequency Fingerprint Convolutional Neural Network and Feature Fusion
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-In-phase/Quadrature(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-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.
Radar emitter identificationDual Radio Frequency Fingerprint Convolutional Neural Network(Dual RFF-CNN2)Feature fusionFingerprint featureRaw-In-phase/Quadrature(Raw-I/Q)signal