RF-AttenNet: A Hybrid Attention-Enhanced Network for Mixed Signal Classification in UAV Swarm Detection

Author

• Prajoy Podder
• Mohammad Atikur Rahman
• MacIej Zawodniok, Missouri University of Science and TechnologyFollow
• Sanjay Madria, Missouri University of Science and TechnologyFollow

Abstract

The continuous increase of UAVs, particularly in swarms, creates significant challenges for security and airspace regulation. Traditional RF fingerprinting methods struggle to detect and classify UAV swarms due to overlapping signals and interference. This study introduces RF-AttenNet, a hybrid deep learning model designed to classify mixed UAV signals by analyzing composite RF spectrograms. RF-AttenNet uses dual attention mechanisms, channel and spatial attention to focus on critical spectral features, enabling the model to effectively separate and identify overlapping UAV signals. We have developed custom composite UAV datasets that simulate real-world swarm interference, incorporating both single and mixed UAV classes. RF-AttenNet achieves state-of-the-art performance, outperforming traditional methods with 95.75% accuracy, 96.41% precision, 95.75% recall, and 95.71% F1-score. RF-AttenNet's ability to separate concurrent UAV signals highlights its effectiveness in complex swarm detection scenarios. RF-AttenNet is scalable and robust, validated on increasingly complex datasets, including up to 10 UAV types, ensuring its applicability to swarm detection and counter-drone operations. This work advances RF-based UAV detection by addressing the challenges posed by overlapping signals in swarm environments and provides a reliable solution for UAV classification in dense airspace.

Recommended Citation

P. Podder et al., "RF-AttenNet: A Hybrid Attention-Enhanced Network for Mixed Signal Classification in UAV Swarm Detection," International Conference on Communication Systems and Networks Comsnets, no. 2026, pp. 482 - 490, Institute of Electrical and Electronics Engineers, Jan 2026.

The definitive version is available at https://doi.org/10.1109/COMSNETS67989.2026.11418162

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

Army Research Laboratory, Grant W911NF-22-2-0208

Keywords and Phrases

Channel attention; composite signal patterns; Multi-UAV classification; RF-AttenNet; spatial attention; UAV swarm detection

International Standard Serial Number (ISSN)

2155-2509; 2155-2487

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2026