Virtual electromagnetic environment modeling based data augmentation for drone signal identification

Virtual electromagnetic environment modeling based data augmentation for drone signal identification

Blog Article

Radio frequency (RF)-based drone identification technologies have the advantages of long effective distances and low environmental dependence, which has become indispensable for drone surveillance systems.However, since drones operate in unlicensed frequency bands, a large number of co-frequency devices exist in these bands, which brings a great challenge to traditional signal identification methods.Deep learning techniques provide a new approach to complete end-to-end signal identification by directly learning the distribution of RF data.In such scenarios, due to the complexity and high dynamics of cent dyyni the electromagnetic environments, a massive amount of data that can reflect the various propagation conditions of drone signals is necessary for a robust neural network (NN) for identifying drones.

In reality, signal acquisition and labeling that meet the above requirements are too costly to implement.Therefore, we propose a virtual electromagnetic environment modeling based data augmentation (DA) method to improve the diversity of drone signal data.The DA method focuses on simulating the spectrograms of drone signals transmitted in real-world environments and randomly generates extra training data in each training epoch.Furthermore, considering the limited processing capability of RF receivers, we modify the original YOLOv5s model to a more lightweight version.

Without losing the identification performance, more hardware-friendly designs apac1/60/1/cw are applied and the number of parameters decreases about 10-fold.For performance evaluation, we utilized a universal software radio peripheral (USRP) X310 platform to collect RF signals of four drones in an anechoic chamber and a practical wireless scenario.Experiment results reveal that the NN trained with augmented data performs as well as that trained with practical data in the complex electromagnetic environment.