A Novel Time-Domain Graph Tensor Attention Network for Specific Emitter Identification

Abstract

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.