Identification of Active Jamming Based on Swin Transformer Model and Splitting Features
Zijun Hu, Xinliang Chen, Zhennan Liang, Bowen Cai · 2022
With the continuous development of Digital Radio Frequency Memory (DRFM) technology, radar working condition is seriously threatened by various activate jamming, echo of true target will be mixed or covered by jamming. In this condition, splitting features extracted by modulating splitting code into the process of pulse compression present greatly difference between true target and jamming, and then this paper proposes a jamming identification method based on splitting feature and Swin Transformer (shifted window Transformer) neural network which can effectively distinguish the typical jamming, achieve classification task, and improve detection performance and recognition accuracy. Finally, the verification result of measured data shows that true target and jamming can be recognized perfectly.