Towards Single-Component and Dual-Component Radar Emitter Signal Intra-Pulse Modulation Classification Based on Convolutional Neural Network and Transformer

In the modern electromagnetic environment, intra-pulse modulations of radar emitter signals have become more complex. Except for single-component signals, dual-component been widely used in current systems. order to make system ability classify and modulation at same period time accurately, this paper, we propose a multi-label learning method based on convolutional neural network transformer. Firstly, original single channel sampled sequences are padded with zeros length. Then converted frequency-domain that only contain amplitude information. After that, data normalization is employed decrease influence amplitude. preprocessing, designed model which combines transformer accomplish classification. The extensive experiments indicate proposed consumes lower computation resources has higher accuracy than other baseline methods classifying eight types thirty-six modulation, where overall weighted beyond 90%.