RF Classification of UAVs Using MLP Model and Software Defined Radio

The use of commercial off-the-shelf (COTS) drones has been exponential growth, whether for leisure or professional purposes. In addition to the beneficial applications, they recently raised security threats for critical and strategic sites such as nuclear power plants, airports, and stadiums. Therefore, the need to detect these targets becomes essential for national security. The chosen approach is based on the detection of the RF signal related to the remote control (RC) link of the drone system using software-defined radio (SDR). The RF fingerprints are then extracted, we have considered the standard deviation (std), kurtosis, skewness, and Peak-to-Average Power Ratio (PAPR). These features and used for classification based on a customized Multi layer perceptron (MLP) model to identify the detected target.