Detection and Classification of Unmanned Aerial Vehicles Based on the Gramian Angular Field and Hilbert Curve

The detection and identification of unmanned aerial vehicles (UAVs) using radio frequency (RF) signals becomes particularly challenging in congested spectral environments, where conventional approaches relying solely on spectral characteristics often prove limited. This work introduces a novel technique for both UAV detection and classification based on temporal representations derived directly from the envelope of received RF signals. The proposed system follows a two-stage architecture: first, binary detection of UAV presence in a given RF channel, and second, identification of the specific UAV model among several commercial platforms. For the first stage, two signal representation methodologies are employed—Gramian Angular Fields and Hilbert curves—both generated from short-time RF windows and subsequently used as inputs to convolutional neural networks. Experimental evaluation demonstrates that the detection stage achieves accuracy rates exceeding 94% for the non-UAV class and approaching 99% for the UAV class with both approaches. In the identification stage, the system attains an accuracy above 90% for most considered UAV models, reaching up to 100% for certain platforms. These results confirm the effectiveness of the envelope-based approach for analyzing UAV-related RF signals.