Drone Classification with a Hybrid Deep Learning Approach Based on Mel-Spectrogram Representation

With technological advancements, the use of drones has become increasingly widespread in both civilian and military sectors in recent years. There is a need for technologies that can detect and identify the presence, type, or flight mode of a drone with remote sensing signals in situations that pose a security threat. This study is based on the classification of radio frequency (RF) signals from various drones under different flight modes using Mel-spectrogram representations. Within the scope of the study, the VGG19 deep learning model is used as both a classifier and a feature extractor for the SVM classifier. On the other hand, the study compares the performance of RF signals in low and high frequency bands separately and in concatenated versions. In the results obtained in the experimental studies, the VGG19+SVM hybrid model showed the highest performance over the Mel-spectrogram of the concatenated low and high (L+H) frequencies. The accuracy performances were 100% in the 2-Class problem where drone presence was detected (drone -no drone), 90.78% in the 4-Class problem where drone types were classification (Bebop-AR-Phantom-no drone), and 86.6% in the 10-Class problem where drone modes were obtained.