Radar Signal Sorting via Graph Convolutional Network and Semi-Supervised Learning

As a key technology in radar reconnaissance systems, radar signal sorting aims to separate multiple radar pulses from an interleaved pulse stream. Supervised signal sorting methods based on deep learning depend on a large volume of training data to optimize model parameters. However, acquiring labeled pulses in practice is challenging. In this letter, a semi-supervised learning (SSL) framework is proposed to address this issue. First, a Self-Organizing Map (SOM) is used to learn the spatial distribution of impulse features, and an anchor graph is constructed based on SOM nodes. A pseudo-label set is then generated using the SOM based on pulse discrepancy information. Finally, a three-layer Weighted Residual Graph Convolutional Network (WRGCN) is designed for signal sorting, with its parameters pre-trained on pseudo-labels and fine-tuned with a limited number of true labels. Experiments on a simulated radar pulse dataset demonstrate that this framework outperforms several existing methods for radar signal sorting with limited labeled pulses.