Radio frequency fingerprint-based drone identification and classification using Mel spectrograms and pre-trained YAMNet neural

Abstract

The convergence of drones with the Internet of Things (IoT) has paved the way for the Internet of Drones (IoD), an interconnected network of drones, ground control systems, and cloud infrastructure that enables enhanced connectivity, data exchange, and autonomous operations. The integration of drones with the IoD has opened up new possibilities for efficient and intelligent aerial operations, facilitating advancements in sectors such as logistics, agriculture, surveillance, and emergency response This paper introduces a novel approach for drone identification and classification by extracting radio frequency (RF) fingerprints and utilizing Mel spectrograms as distinctive patterns. The proposed approach converts RF signals to audio signals and leverages Mel spectrograms as essential features to train neural networks. The YAMNet neural network is employed, utilizing transfer learning techniques, to train the dataset and classify multiple drone models. The initial classification layer achieves an impressive accuracy of 99.6% in distinguishing between drones and non-drones. In the subsequent layer, the model achieves 96.9% accuracy in identifying drone types from three classes, including AR Drone, Bebop Drone, and Phantom Drone. At the third classification layer, the accuracy ranges between 96% and 97% for identifying the specific mode of each drone type. This research showcases the efficacy of Mel spectrogram-based RF fingerprints and demonstrates the potential for accurate drone identification and classification using pre-trained YAMNet neural networks.