Attention-Enhanced Domain Adversarial Training for Robust Automatic Modulation Classification of Radar Signals
DOI:
https://doi.org/10.15837/ijccc.2026.3.7097Keywords:
Automatic Modulation Classification, Radar Signal, Attention Mechanism, Domain Adversarial LearningAbstract
Automatic modulation recognition has a wide range of applications in the field of signal processing. Real-world signal environments are complex and variable, and multiple datasets with domain differences are formed due to different sampling frequencies. However, existing methods usually rely on a single data domain for training, which makes it difficult to adapt to domains with inconsistent distributions. To address this, this paper proposes an attention-enhanced domain adversarial training (AM-DAT) method. Initially, radar signals are transformed into two-dimensional timefrequency images via the Smoothed Pseudo Wigner-Ville Distribution (SPWVD). Subsequently, discriminative and robust features are effectively extracted by the attention-enhanced neural network; while domain adversarial learning strategy is combined to achieve the consistency of feature distributions in the source and target domains, thus improving the generalization ability of the model to data domains with different distributions. Experimental results show that AM-DAT achieves superior classification accuracy across signals-to-noise ratio (SNRs), and its performance is much higher than the methods lacking adversarial training mechanism under low SNR (-2 dB). Our approach demonstrates strong potential for practical radar signal classification applications.
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