RF-Based Drone Detection with Deep Neural Network: Review and Case Study

Drones have been widely used in many application scenarios, such as logistics and on-demand instant delivery, surveillance, traffic monitoring, firefighting, photography, and recreation. On the other hand, there is a growing level of misemployment and malicious utilization of drones being reported on a local and global scale. Thus, it is essential to employ security measures to reduce these risks. Drone detection is a crucial initial step in several tasks such as identifying, locating, tracking, and intercepting malicious drones. This paper reviews related work for drone detection and classification based on deep neural networks. Moreover, it presents a case study to compare the impact of utilizing magnitude and phase spectra as input to the classifier. The results indicate that prediction performance is better when the magnitude spectrum is used. However, the phase spectrum can be more resilient to errors due to signal attenuation and changes in the surrounding conditions.