Global Context-Based Threshold Strategy for Drone Identification Under the Low SNR Condition

Regulation of drones is already an important research topic. The drone identification method based on the radio frequency (RF) signal analysis technology is an efficient approach to regulate mainstream civilian drones. However, existing research on drone identification has been conducted under high signal-to-noise ratio (SNR) conditions. In fact, long-range drone identification in urban environments is performed under low SNR conditions. In this article, a global context (GC)-based threshold strategy is proposed to solve the above problem. The threshold is automatically determined during the deep architecture optimization. Meanwhile, the unimportant features in the network are removed by threshold denoising. First, the weight of each feature channel is obtained by modeling cross-channel dependencies, and then a weighted average is performed for each position along the feature channel on the feature map to obtain the thresholds. The method reduces the impact of unimportant features on performance without complex data preprocessing. In the experiments, the performance and complexity of the proposed method are extensively evaluated by 11 different drone RF signals. The experimental results show that in comparison with other deep learning-based threshold calculation methods, the proposed method has a more outstanding performance and lower computational complexity for drone RF signal identification under the low SNR condition.