Few-Shot Active Radar Jamming Recognition Using Adaptive Confidence Dual-Branch Fusion Network with Full-Information Time–Frequency Features

Active radar jamming recognition is an important technology in modern electronic warfare (EW). With the increasingly widespread application of Digital Radio Frequency Memory (DRFM) technology, the types of jamming have become more diverse. Meanwhile, in most non-cooperative confrontation scenarios, it is difficult to obtain large-scale labeled jamming samples. The commonly used methods for radar jamming signal recognition have some limitations. One is that they require a large number of jamming samples; the other is that their recognition performance degrades severely in few-shot scenarios. To address these limitations, an Adaptive Confidence Dual-Branch Fusion Network (ACDF-Net) is proposed for few-shot jamming recognition. The Short-Time Fourier Transform (STFT) is firstly used to obtain four-channel time–frequency features of the jamming signal, which fully retains the real part, imaginary part, amplitude, and phase information of jamming. Secondly, a dual-branch complementary representation learning network is well designed to extract jamming modulation characteristics and energy-phase characteristics. Finally, an adaptive confidence fusion method controlled by learnable parameters and a multi-task supervised learning paradigm are introduced, which improve recognition accuracy and make the network more robust. A mixed dataset containing 15 types of radar active jamming (including 8 single jamming types, 3 composite jamming types, and 4 real measured jamming types) is established to verify the proposed method. Multi-dimensional validation experiments are conducted, including few-shot performance comparison under different training set proportions (3~11%), per-class recognition performance analysis, measured data generalization validation, and ablation studies of core modules. In few-shot scenarios, the OA of ACDF-Net reaches 92.81% under 3% training data proportion, which is better than the comparison algorithms. More comparative experiments show that the proposed method is better than the comparison methods. The proposed method provides an effective solution for radar active jamming recognition in few-shot and complex electromagnetic environments, which exhibits high feasibility for practical engineering applications.