SAHG-WMRNet: A Structure-Aware Hierarchical Graph Network for Multifunction Radar Work Mode Recognition

The pulse sequence-based multifunction radar (MFR) work mode recognition (WMR) is crucial for cognitive electronic warfare. However, the missing and spurious pulses, measurement errors, and limited labeled samples pose severe challenges to the WMR task. SAHG-WMRNet is proposed based on self-supervised pulse group segmentation and semi-supervised adaptive hierarchical graph nodes recognition. Firstly, pulse sequence parameters are encoded with multiple feature maps in the high inter-class separable latent space through self-supervised multi-scale encoder, and the differences among feature maps are utilized to automatically segment the pulse sequence into multiple pulse groups with their individual modulation types via segmentation manner. Then, the initial pulse-level graph is constructed based on feature maps via K-nearest neighbor algorithm. Finally, a pulse and pulse group-level hierarchical graph is constructed through adaptive coarsening and refining based on the pulse group segmentation results. By using hierarchical graph, the WMR task can be implemented in a semi-supervised manner, utilizing a small amount of labeled data and explicit geometric relationships. The simulation results in 1-shot and 5-shot scenarios show that the proposed method can achieve better recognition accuracy with limited labeled samples under non-ideal scenarios, compared to some existing methods.