Combined RF-Based Drone Detection and Classification

Abstract

Abstract: Despite various positive uses, drones are also utilised for illegal purposes like as drug trafficking, weapon smuggling, and posing dangers to security-sensitive locations such as airports and nuclear power plants. Existing drone localisation and neutralisation solutions are predicated on the drone having previously been discovered and categorised. Despite significant progress in the sensor sector over the last decade, no viable drone detection and classification approach has been suggested in the literature. The frequency signature of the sent signal is used in this research to identify and classify drones using radio frequency (RF). Using commercial drones, we built an unique drone RF dataset and provided a comprehensive comparison of a two-stage and integrated detection and classification framework. Both frameworks' detection and classification results are demonstrated for a single-signal and simultaneous multi-signal situation. We demonstrate that the You Only Look Once (YOLO) framework outperforms the Goodness-of-Fit (GoF) spectrum sensing framework in a simultaneous multi-signal situation, and that it outperforms the Deep Residual Neural Network (DRNN) framework in classification