论文标题
基于异构雷达网络的飞机识别的时空频率图卷积网络
Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Network
论文作者
论文摘要
本文提出了一个知识和数据驱动的基于图形神经网络的协作学习模型，用于在异质雷达网络中可靠的飞机识别。飞机可识别性分析表明：（1）飞机的语义特征是动态特性驱动的运动模式，以及（2）雷达横截面（RCS）信号中包含的语法特征呈现出空间 - 周期性频率（STF）的多样性，由电子辐射形状和飞机的电子辐射形状模式决定。然后开发了STF图注意力卷积网络（STFGACN），以从异质雷达网络接收到的RCS信号中提炼语义特征。广泛的实验结果验证了STFGACN在检测准确性方面胜过基线方法，并进行消融实验，以进一步表明，信息维度的扩展可以在低信噪比区域中获得可靠的效果，从而获得相当大的益处。
This paper proposes a knowledge-and-data-driven graph neural network-based collaboration learning model for reliable aircraft recognition in a heterogeneous radar network. The aircraft recognizability analysis shows that: (1) the semantic feature of an aircraft is motion patterns driven by the kinetic characteristics, and (2) the grammatical features contained in the radar cross-section (RCS) signals present spatial-temporal-frequency (STF) diversity decided by both the electromagnetic radiation shape and motion pattern of the aircraft. Then a STF graph attention convolutional network (STFGACN) is developed to distill semantic features from the RCS signals received by the heterogeneous radar network. Extensive experiment results verify that the STFGACN outperforms the baseline methods in terms of detection accuracy, and ablation experiments are carried out to further show that the expansion of the information dimension can gain considerable benefits to perform robustly in the low signal-to-noise ratio region.
