An Ensemble Learning-Based Transformer for Radar Jamming Recognition with Insufficient Samples

Radar jamming recognition is a fundamental step for anti-jamming techniques. However, the recognition performance is impeded in insufficient samples situation. Ensemble learning provides an effective way to address this issue. In this paper, first, a novel ensemble learning-based radar Transformer (i.e., RadarTR-E) is proposed to improve recognition performance with insufficient samples. Specifically, it votes on the predictions among sub-recognizers to increase recognition accuracy, where RadarTR is employed to effectively capture long-range dependencies. Then, a dynamic label smoothing method (i.e., RadarTR-E-DLS) is further proposed to mitigate overfitting. In detail, dynamic soft labels are designed to prevent overconfidence towards certain radar jamming types. Therefore, the proposed RadarTR-E-DLS achieves better recognition accuracy. Compared with other advanced methods, the experimental results show the superior recognition performance of the proposed methods.