Knowledge-Guided BiTCN Prototypical Network for Few-Shot Radar HRRP Target Recognition

The high-resolution range profile (HRRP) plays an important role in radar automatic target recognition (RATR) due to its rich target structure information and small data volume. However, it is difficult for HRRP to obtain sufficient data, especially for non-cooperative target, so few-shot HRRP target recognition methods have emerged. Inspired by human cognitive neuroscience, we compensate for the sample shortage by introducing semantic knowledge, focusing on key distinguishing features of categories while ignoring noise through knowledge guidance. To this end, we propose a knowledge-guided BiTCN prototypical network, where semantic knowledge is presented in the form of knowledge graph embeddings, HRRP feature extraction is completed using the temporal model BiTCN (bidirectional temporal convolutional network), and then knowledge-guided attention is used to enhance class prototypes. The experimental results demonstrate the effectiveness of our method.