Multi‐label hybrid radar signal recognition based on a feature pyramid network and class activation mapping
Weijian Si, Jiaji Luo, Zhian Deng
Abstract
Abstract The electromagnetic environment of modern battlefields becomes increasingly complex, and radar receivers may receive multiple radar signals simultaneously. However, current deep learning models can only predict a single class and cannot recognize multi‐label mixed radar signals. In this study, a multi‐label hybrid radar signal recognition framework based on the feature pyramid network (FPN) and class activation map (CAM) is proposed. The multi‐label radar signals are recognized by calculating the average value of the CAM corresponding to each class. The proposed method can recognize, localize and separate mixed radar signals in time‐frequency images, which improves the interpretability and transparency of the model. In addition, the FPN is adopted to improve the spatial resolution of the feature maps, and the Mixup data augmentation is utilized to improve the generalization performance of the model. Experiments with eight different modulation types of mixed radar signals show that the recognition accuracy of hybrid radar signals achieves 92.2% at 0 dB.