A Hybrid Network-Based Contrastive Self-Supervised Learning Method for Radar Signal Modulation Recognition

The recognition accuracy in radar signal modulation recognition (RSMR) is severely impacted by the lack of training data. Conventional deep learning approaches typically depend on extensive labeled datasets, which are scarce in real-world scenarios. To mitigate this limitation, a convolutional-transformer hybrid network based on self-supervised contrastive learning (CTNet-SSCL) is proposed for RSMR. In self-supervised contrastive pre-training, an amplitude distortion data augmentation technique is proposed, which enables the model to effectively utilize unlabeled data, allowing it to learn meaningful feature representations. Subsequently, the pre-trained multi-scale perceptual transformer (MSPFormer) encoder, combined with a randomly initialized classifier, is fine-tuned using labeled samples. The encoder combines multi-scale feature fusion and time-frequency attention mechanisms to further enhance the robustness and recognition accuracy of the model in complex environments. The excellent performance of the proposed method is verified in experiments on a dataset with 10 different waveforms. The recognition accuracy of the proposed method reaches 99.98% at a signal-to-noise ratio (SNR) of 2 dB. The source code is publicly accessible at https://github.com/wanan0414/CTNet-SSL.