论文标题
多方面的图形注意网络，用于异质雷达网络中的雷达目标识别
Multi-faceted Graph Attention Network for Radar Target Recognition in Heterogeneous Radar Network
论文作者
论文摘要
雷达目标识别（RTR）是智能雷达系统的关键技术，已得到充分研究。低信噪比（SNR）的准确RTR仍然是一个开放的挑战。大多数现有的方法基于单个雷达或同质雷达网络，这些网络无法完全利用频率信息。在本文中，提出了一个两流语义特征融合模型，称为多面图网络（MF-GAT），以极大地提高异质雷达网络的低SNR区域的准确性。通过融合从源域提取的功能并通过图形注意网络模型转换域，MF-GAT模型在统一框架中分类前会提取更高级别的语义特征。提出了广泛的实验，以证明所提出的模型可以大大改善低SNR的RTR性能。
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.
