Multi-resolution convolutional neural network for specific emitter identification
Tianshu Cui, R. Li, Zhihao Li, Liang Shi, Hongjiang Zhang · 2024
Specific Emitter Identification (SEI) distinguishes specific emitters among identical radar types by inspecting hardware fingerprints. Nevertheless, deep learning-based SEI poses challenges due to an excessive count of model parameters and high computational complexities, hindering its applicability in real-time and near-real-time situations. To address these challenges, we introduce a Multi-Resolution Convolutional Network (MRNet) capable of extracting signal features across both time and frequency domains. This model employs dilated convolution at varying expansion rates to glean multi-resolution features of signals, thereby enhancing accuracy without increasing parameters or computational efforts. An experiment conducted with ten real radar emitters examines aspects such as the depth and width of the convolutional network. The results reveal that MRNet outperforms conventional convolutional networks, achieving an approximate 8% boost in accuracy. When compared with multi-scale networks, MRNet exhibits superior performance with diminished complexity. This study emphasizes the advantages of employing MRNet in SEI, particularly in enhancing efficiency and interpretability.