UAV Detection using Convolutional Neural Networks and Radio Frequency Data

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly seen as significant threats to both civilian and military installations, highlighting the need for effective UAV detection systems. Among the various detection methods proposed, passive radio frequency sensing has proven particularly effective. In this study, we utilized an open radio frequency dataset to explore the use of Convolutional Neural Networks (CNNs) for UAV detection. Our comparison with traditional machine learning models, such as random forest, AdaBoost, and XGBoost, demonstrates that CNNs offer superior performance. Specifically, CNNs achieved impressive accuracy rates: 99.93% for detecting UAVs, 93.30% for classifying UAV types, and 76.43% for categorizing UAV modes. These results underscore the potential of CNNs as a powerful tool for UAV detection and classification.