High Resolution Range Profiles (HRRP) 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 HRRP as sequences, which may lead to ignorance of the internal relationship of range cells. This letter introduces HRRPGraphNet, whose pivotal innovation is the transformation of HRRP data into a novel graph structure, utilizing a range cell amplitude(hyphen)based node vector and a range(hyphen)relative adjacency matrix. This graph(hyphen)based approach facilitates both local feature extraction via one(hyphen)dimensional convolution layers, global feature extraction through a graph convolution layer and a attention module. Experiments on the aircraft electromagnetic simulation dataset confirmed HRRPGraphNet superior accuracy and robustness, particularly in limited training sample environments, underscoring the potential of graph(hyphen)driven innovations in HRRP(hyphen)based RATR.
@article{arxiv.2407.08236,
title = {HRRPGraphNet: Make HRRPs to Be Graphs for Efficient Target Recognition},
author = {Lingfeng Chen and Xiao Sun and Zhiliang Pan and Zehao Wang and Xiaolong Su and Zhen Liu and Panhe Hu},
journal= {arXiv preprint arXiv:2407.08236},
year = {2026}
}
Comments
3 pages, 3 figures. Accepted by IET Electronics Letters