关键词: 电磁感知, 雷通一体化, 人工智能, 无监督预训练

Abstract: In the field of electronic countermeasures, a situation was faced where there is a large volume of data with few samples, and radar and communication signals are modeled separately. This has led to fragmentation in capabilities and low data utilization rates. To address this issue, an unsupervised electromagnetic pre-training model scheme was designed based on the CNN-transformer as the main network structure. The massive amount of unlabeled electromagnetic data collected through reconnaissance was fully leveraged to learn the distribution of signal features, preventing the model from overfitting on a small number of specific samples. As a result, data utilization and the generalization ability of the recognition model are improved. Furthermore, considering the multi-source data scenario in reconnaissance data, which includes both real-numbered radar data and complex-numbered communication data, the CNN-transformer-based pre-training model was innovatively transformed into a complex number representation. A complex CNN-transformer-based electromagnetic pre-training model was designed,achieving integrated joint modeling of radar and communication data. Additionally, by adopting the technical route of complex CNN-transformer pre-training model + backend fine-tuning, multiple electromagnetic perception and recognition tasks were successfully modeled, including radar individual identification, modulation style recognition, and communication individual identification. This effectively resolves the issue of capability fragmentation. Experimental results show that compared to deep learning models based on real-number networks, On the non-homologous test set, the recognition accuracy was improved by an average of more than 10 percentage points, greatly enhancing the model's accuracy, generalizability, and uniformity.

Key words: Electromagnetic Perception, Radar-Communication Integration, Artificial Intelligence, Unsupervised Pretraining