Radar Signal Recognition and Localization Based on Multiscale Lightweight Attention Model

Abstract

The recognition technology of the radar signal modulation mode plays a critical role in electronic warfare, and the algorithm based on deep learning has significantly improved the recognition accuracy of radar signals. However, the convolutional neural networks became increasingly sophisticated with the progress of deep learning, making them unsuitable for platforms with limited computing resources. ResXNet, a novel multiscale lightweight attention model, is proposed in this paper. The proposed ResXNet model has a larger receptive field and a novel grouped residual structure to improve the feature representation capacity of the model. In addition, the convolution block attention module (CBAM) is utilized to effectively aggregate channel and spatial information, enabling the convolutional neural network model to extract features more effectively. The input time-frequency image size of the proposed model is increased to $600\times 600$ , which effectively reduces the information loss of the input data. The average recognition accuracy of the proposed model achieves 91.1% at -8 dB. Furthermore, the proposed model performs better in terms of unsupervised object localization with the class activation map (CAM). The classification information and localization information of the radar signal can be fused for subsequent analysis.