RF-Based UAV Detection and Classification Using ANN Models

The rise of low-cost drones, or Unmanned Aerial Vehicles (UAVs), has transformed industries like agriculture, disaster response, and infrastructure inspection. However, this increase poses considerable issues for safety, security, and airspace management. To address these challenges, radiofrequency (RF)-based approaches are emerging as potential options for real-time UAV identification and classification since they are non-intrusive and compatible with existing systems. This research offers a unique Artificial Neural Network (ANN) model for UAV classification that includes efficient preprocessing and feature extraction approaches. The suggested architecture reduces feature dimensionality using Principal Component Analysis (PCA) and improves classification performance with statistical and frequency-domain characteristics like Root Mean Square (RMS) and Zero-Crossing Rate (ZCR). Experimental evaluations of the DroneRF dataset show that the proposed ANN is superior, with classification accuracies of 99.46%, 98.52%, and 83.40% for 2-class, 4-class, and 10-class tasks, respectively. This work is a big step toward robust, real-time drone detection and classification systems.