Improved Aligned Variational Autoencoders with Knowledge Graph for Generalized Zero-Shot Radar HRRP Target Recognition

With the rapid development of deep learning, significant progress has been made in radar high-resolution range profile (HRRP) target recognition methods based on deep neural networks. However, these closed set recognition methods assume that the categories of all targets are known during the model training phase, while in practice seen and unseen class targets coexist. Therefore, we propose a novel method for generalized zero-shot HRRP target recognition. Specifically, we use knowledge graph as auxiliary semantic information, utilizing two variational auto-encoders (VAEs) for cross-modal alignment and distribution alignment of semantic embeddings and HRRP features, and adopting a joint learning method of feature alignment and classification to enhance the separability of HRRP features for different categories. The experimental results demonstrate that our method is superior to existing methods.