Authors:
Stefan Glüge
1
;
Matthias Nyfeler
1
;
Nicola Ramagnano
2
;
Claus Horn
1
and
Christof Schüpbach
3
Affiliations:
1
Institute of Computational Life Sciences, Zurich University of Applied Sciences, 8820 Wädenswil, Switzerland
;
2
Institute for Communication Systems, Eastern Switzerland University of Applied Sciences, 8640 Rapperswil-Jona, Switzerland
;
3
armasuisse Science + Technology, 3602 Thun, Switzerland
Keyword(s):
Deep Learning, Robustness, Signal Detection, Unmanned Aerial Vehicles.
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.
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