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This document presents a novel multimodal deep learning approach for detecting unmanned aerial vehicles (UAVs) using radio frequency (RF) and acoustic signals. The proposed method combines traditional AI algorithms with deep learning models, achieving a classification accuracy of 92% in low signal-to-noise ratio environments. The study emphasizes the effectiveness of integrating RF and audio features to enhance drone detection efficiency and addresses the limitations of existing detection methods.

Author

ehabahussein

Language

EN