FLEDNet: Enhancing the Drone Classification in the Radio Frequency Domain
Researchers are actively pursuing advancements in convolutional neural networks and their application in anti-drone systems for drone classification tasks. Our study investigates the hypothesis that the accuracy of drone classification in the radio frequency domain can be enhanced through a hybrid a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/9/4/243 |
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| Summary: | Researchers are actively pursuing advancements in convolutional neural networks and their application in anti-drone systems for drone classification tasks. Our study investigates the hypothesis that the accuracy of drone classification in the radio frequency domain can be enhanced through a hybrid approach. Specifically, we aim to combine fuzzy logic for edge detection in images (the spectrograms of drone radio signals) with convolutional and convolutional recurrent neural networks for classification tasks. The proposed FLEDNet approach introduces a tailored engineering strategy designed to tackle classification challenges in the radio frequency domain, particularly concerning drone detection, the identification of drone types, and multiple drone detection, even within varying signal-to-noise ratios. The strength of this tailored approach lies in implementing a straightforward edge detection method based on fuzzy logic and simple convolutional and convolutional recurrent neural networks. The effectiveness of this approach is validated using the publicly available VTI_DroneSET dataset across two different frequency bands and confirmed through practical inference on the embedded computer NVIDIA Jetson Orin NX with radio frequency receiver USRP-2954. Compared to other approaches, FLEDNet demonstrated a 4.87% increase in accuracy for drone detection, a 13.41% enhancement in drone-type identification, and a 7.26% rise in detecting multiple drones. This enhancement was achieved by integrating straightforward fuzzy logic-based edge detection methods and neural networks, which led to improved accuracy and a reduction in false alarms of the proposed approach, with potential applications in real-world anti-drone systems. The FLEDNet approach contrasts with other research efforts that have employed more complex image processing methodologies alongside sophisticated classification models. |
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| ISSN: | 2504-446X |