Abstract
Despite several beneficial applications, unfortunately, drones are also
being used for illicit activities such as drug trafficking, firearm
smuggling or to impose threats to security-sensitive places like
airports and nuclear power plants. The existing drone localization and
neutralization technologies work on the assumption that the drone has
already been detected and classified. Although we have observed a
tremendous advancement in the sensor industry in this decade, there is
no robust drone detection and classification method proposed in the
literature yet. This paper focuses on radio frequency (RF) based drone
detection and classification using the frequency signature of the
transmitted signal. We have created a novel drone RF dataset using
commercial drones and presented a detailed comparison between a
two-stage and combined detection and classification framework. The
detection and classification performance of both frameworks are
presented for a single-signal and simultaneous multi-signal scenario.
With detailed analysis, we show that You Only Look Once (YOLO) framework
provides better detection performance compared to the Goodness-of-Fit
(GoF) spectrum sensing for a simultaneous multi-signal scenario and good
classification performance comparable to Deep Residual Neural Network
(DRNN) framework.