RBViT: Enhanced Versatile Representations and Noise Resistance Vision Transformer for Modulation Recognition

In modern electromagnetic reconnaissance, the ability to recognise intra‐pulse modulation patterns in complex environments has become increasingly critical. Traditional recognition algorithms are typically designed under ideal conditions and handcrafted features, and the presence of substantial noise interference in complex electromagnetic conditions can significantly degrade the performance of these algorithms. Although transformers have achieved great success in computer vision tasks, existing vision transformers lack the ability to capture long‐range dependencies. To remedy these flaws, this paper proposes an accurate automatic modulation recognition algorithm based on the vision transformer. First, we utilise traditional image processing techniques to provide the position of signals in the time frequency image and introduce a radial basis function to generate the positional embeddings. This procedure enhances the spatial awareness of the model. Second, dynamic scales of 2D patches are embedded as inputs to the encoders. Furthermore, inspired by dilated convolutions, vanilla attention is reformed in the cascade encoders to promote the training and inference procedures. Experiments and ablation studies exhibit the proposed algorithm that performs well across various signal‐to‐noise ratio conditions, demonstrating its robustness and accuracy in modulation recognition.