Radar target recognition (RTR), as a key technology of intelligent radar systems, has been well investigated. Accurate RTR at low signal-to-noise ratios (SNRs) still remains an open challenge. Most existing methods are based on a single radar or the homogeneous radar network, which do not fully exploit frequency-dimensional information. In this paper, a two-stream semantic feature fusion model, termed Multi-faceted Graph Attention Network (MF-GAT), is proposed to greatly improve the accuracy in the low SNR region of the heterogeneous radar network. By fusing the features extracted from the source domain and transform domain via a graph attention network model, the MF-GAT model distills higher-level semantic features before classification in a unified framework. Extensive experiments are presented to demonstrate that the proposed model can greatly improve the RTR performance at low SNRs.
@article{arxiv.2206.05168,
title = {Multi-faceted Graph Attention Network for Radar Target Recognition in Heterogeneous Radar Network},
author = {Han Meng and Yuexing Peng and Wei Xiang and Xu Pang and Wenbo Wang},
journal= {arXiv preprint arXiv:2206.05168},
year = {2022}
}