Specific Emitter Identification Based on RepVGG and Gramian Angular Field

Abstract

This paper presents a new method for specific emitter identification (SEI) using the re-parameterization 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.

Keywords

specific emitter identification / re-parameterization visual geometry group (RepVGG) / Gramian angular field