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| Research on UAV RF Signal Detection Based on FEMA-YOLO |
| ZHENG Tao， WANG Jinming |
| College of Communication Engineering， Army Engineering University of PLA， Nanjing 210007， Jiangsu， China |
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Abstract To address the challenges of unmanned aircraft system ( UAV ) identification in complex electromagnetic environments, this paper proposes a feature-enhanced multi-scale attention YOLO (FEMA-YOLO) algorithm for UAV radio frequency (RF) signal detection. Firstly, the acquired RF signals of UAVs are preprocessed, and image encoding is performed through short-time Fourier transform (STFT) to form time-frequency representations. Secondly, a bidirectional feature pyramid network (BiFPN) and a multi-scale attention network (EMA) are constructed to enhance the feature extraction capability for RF signal details and improve the anti-interference capability against background noise. Finally, the time-frequency features are fed into the improved FEMA-YOLO model for unmanned aerial vehicle (UAV) type identification. Experimental results demonstrate that the proposed algorithm can effectively identify six types of UAVs, achieving a recognition accuracy of 92.1% and a mean average precision (mAP) of 93.8%.
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Received: 25 June 2025
Published: 04 August 2026
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2026, Vol. 9 
