In recent years, Deep Learning (DL) has been successfully applied to detect and classify
Radio Frequency (RF) Signals. A DL approach is especially useful since it identifies the …

Unmanned aerial vehicles (UAVs), or drones, have revolutionized a wide range of
industries, including monitoring, agriculture, surveillance, and supply chain. However, their …

As a potential air control measure, RF-based surveillance is one of the most commonly used
unmanned aerial vehicles (UAV) surveillance methods that exploits specific emitter …

The use of supervised learning with various sensing techniques such as audio, visual
imaging, thermal sensing, RADAR, and radio frequency (RF) have been widely applied in …

Unmanned aerial vehicle (UAV) sensing based on the emitted radio frequency (RF) signals
is investigated for UAV surveillance and control. A novel robust RF-based UAV sensing …

With the increasing popularity of civilian unmanned aerial vehicles (UAVs), safety issues
arising from unsafe operations and terrorist activities have received growing attention. To …

We propose a novel approach for drone detection and classification based on RF
communication link analysis. Our approach analyses large signal record including several …

Specific emitter identification (SEI) refers to distinguishing emitters using individual features
extracted from wireless signals. The current SEI methods have proven to be accurate in …

Abstract The deployment of Unmanned Aerial Vehicles (UAVs) has gained increasing
interest in various applications during recent years due to its multiple features. At the same …

As unmanned aerial vehicles (UAVs), owing to their low cost and wide variety of
applications, become increasingly indispensable to modern society, and monitoring their …