Identification of unmanned aerial vehicles using RF fingerprinting and deep learning networks

Abstract

This paper explores key challenges in machine learning classification for optimizing the identification of unmanned aerial vehicles (UAVs) using radio frequency (RF) features and introduces an improved approach based on specific fingerprinting techniques. The study begins by discussing essential data preprocessing steps and feature extraction techniques relevant to RF-based signal analysis for UAVs identification. Several RF feature representations—such as Power Spectral Density (PSD), Short-Time Fourier Transform (STFT), and wavelet-based methods—are tested and compared. The proposed strategy is evaluated on an open-source dataset using different machine learning classifiers. Results indicate that convolutional neural networks (CNNs), when paired with wavelet-based feature extraction, offer the highest classification accuracy, making it possible to differentiate UAV types more effectively. These findings underscore the growing role of deep learning in RF-based UAV identification, with important implications for security and spectrum monitoring. © 2025 Copyright for this paper by its authors.