HRRPGraphNet: Make HRRPs to be graphs for efficient target recognition

Abstract

High Resolution Range Profiles (HRRPs) have become a key area of focus in the domain of Radar Automatic Target Recognition (RATR). Despite the success of deep learning based HRRP recognition, these methods needs a large amount of training samples to generate good performance, which could be a severe challenge under non-cooperative circumstances. Currently, deep learning based models treat HRRPs as sequences, which may lead to ignorance of the internal relationship of range cells. This letter proposes HRRPGraphNet, a novel graph-theoretic approach, whose primary innovation is the use of the graph-theory of HRRP which models the spatial relationships among range cells through a range cell amplitude-based node vector and a range-relative adjacency matrix, enabling efficient extraction of both local and global features in noneuclidean space. Experiments on the aircraft electromagnetic simulation dataset confirmed HRRPGraphNet's superior accuracy and robustness compared with existing methods, particularly in limited training sample condition. This underscores the potential of graph-driven innovations in enhancing HRRP-based RATR, offering a significant advancement over sequence-based methods.