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 infra
structure 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 clas
sification using pre-trained YAMNet neural networks. |
