Drone detection and classification model based on deep attention by using the RF signals
Xiubo Jia, Lei Wu, Houtang Hao, Jing Liu · 2022
The illegal flight of drones have occurred frequently, which pose a great threat to the public security, and there is an urgent demand to develop related detection and identification technology to prevent such illegal behavior. In this paper, we analyze the principle and influencing factors of drone RF recognition, and propose a drone RF recognition method based on the deep attention mechanism. In our work, a fully convolutional network with the encoder-decoder architecture is adopted, and the residual network is used as the backbone network for feature extraction. Moreover, an RF channel attention aggregation (FCA) module is specifically designed for recognition. Our model was trained and verified on a public dataset (DroneRF Dataset), and achieved 99.895% accuracy for drone detection, more than 98.61% accuracy for drone recognition, and more than 99.33% accuracy for drone working mode recognition. On the self-test dataset, the performance of the RF recognition method proposed in this paper was further verified using the transfer learning approach.