图形
计算机科学
特征(语言学)
编码
张量(固有定义)
图论
时域
模式识别(心理学)
理论计算机科学
算法
人工智能
数学
基因
纯数学
化学
哲学
组合数学
生物化学
语言学
计算机视觉
作者
Haozhe Li,Yilin Liao,Wenhai Wang,Hui Junpeng,Jiaqi Liu,Xinggao Liu
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-14
被引量:6
标识
DOI:10.1109/tim.2023.3241976
摘要
Specific emitter identification (SEI) is significant in military communication scenarios, cognitive radio, and self-organized networks. However, these methods only consider the feature of signals or the feature after signal transformation. In other words, the time-domain correlation of each feature and relationships between features are seldom taken into account. A novel method is, therefore, proposed, which includes a transformation to convert the specific emitter signal into a graph tensor and a model named time-domain graph tensor attention network (TDGTAN) to encode graph tensors for SEI. Specifically, the model includes two main parts. The first part is intrapropagation, which uses the relationship between different sampling points through message propagation in each graph. The other part is interpropagation, which propagates cross-layer messages between different graphs at the same sampling point, to realize the use of the relationship between different features. Extensive experiments are conducted on a real-world dataset, and the result shows that the proposed approach acquires higher accuracy (ACC) and intriguing anti-interference performance. In addition, the proposed model also has higher parameter utilization and calculation efficiency.
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