This paper presents a new method for specific emitter identification(SEI)using the reparameterization visual geometry group(RepVGG)neural network model and Gramian angular summation field(GASF).It converts in-phase an...This paper presents a new method for specific emitter identification(SEI)using the reparameterization visual geometry group(RepVGG)neural network model and Gramian angular summation field(GASF).It converts in-phase and quadrature(IQ)signals into 2D feature maps,retaining both time and frequency domain features.Compared to residual network 18-layer(ResNet18)and Hilbert transform methods,this approach offers higher accuracy,faster training,and a smaller model size,making it ideal for hardware deployment.展开更多
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘This paper presents a new method for specific emitter identification(SEI)using the reparameterization visual geometry group(RepVGG)neural network model and Gramian angular summation field(GASF).It converts in-phase and quadrature(IQ)signals into 2D feature maps,retaining both time and frequency domain features.Compared to residual network 18-layer(ResNet18)and Hilbert transform methods,this approach offers higher accuracy,faster training,and a smaller model size,making it ideal for hardware deployment.
