Robust Drone Detection and Classification from Radio Frequency Signals Using Convolutional Neural Networks

Stefan Glüge, Matthias Nyfeler, Nicola Ramagnano, Claus Horn, Christof Schüpbach

2023

Abstract

As the number of unmanned aerial vehicles (UAVs) in the sky increases, safety issues have become more pressing. In this paper, we compare the performance of convolutional neural networks (CNNs) using first, 1D in-phase and quadrature (IQ) data and second, 2D spectrogram data for detection and classification of UAVs based on their radio frequency (RF) signals. We focus on the robustness of the models to low signal-to-noise ratios (SNRs), as this is the most relevant aspect for a real-world application. Within an input type, either IQ or spectrogram, we found no significant difference in performance between models, even as model complexity increased. In addition, we found an advantage in favor of the 2D spectrogram representation of the data. While there is basically no performance difference at SNRs ≥ 0 dB, we observed a 100% improvement in balanced accuracy at -12 dB, i.e. 0:842 on the spectrogram data compared to 0:413 on the IQ data for the VGG11 model. Together with an easy-to-use benchmark dataset, our findings can be used to develop better models for robust UAV detection systems.

Paper Citation

in Harvard Style

Glüge S., Nyfeler M., Ramagnano N., Horn C. and Schüpbach C. (2023). Robust Drone Detection and Classification from Radio Frequency Signals Using Convolutional Neural Networks. In Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: NCTA; ISBN 978-989-758-674-3, SciTePress, pages 496-504. DOI: 10.5220/0012176800003595

in Bibtex Style

@conference{ncta23,
author={Stefan Glüge and Matthias Nyfeler and Nicola Ramagnano and Claus Horn and Christof Schüpbach},
title={Robust Drone Detection and Classification from Radio Frequency Signals Using Convolutional Neural Networks},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: NCTA},
year={2023},
pages={496-504},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012176800003595},
isbn={978-989-758-674-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: NCTA
TI - Robust Drone Detection and Classification from Radio Frequency Signals Using Convolutional Neural Networks
SN - 978-989-758-674-3
AU - Glüge S.
AU - Nyfeler M.
AU - Ramagnano N.
AU - Horn C.
AU - Schüpbach C.
PY - 2023
SP - 496
EP - 504
DO - 10.5220/0012176800003595
PB - SciTePress