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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29214
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dc.contributor.authorGlüge, Stefan-
dc.contributor.authorNyfeler, Matthias-
dc.contributor.authorRamagnano, Nicola-
dc.contributor.authorHorn, Claus-
dc.contributor.authorSchüpbach, Christof-
dc.date.accessioned2023-11-24T15:21:55Z-
dc.date.available2023-11-24T15:21:55Z-
dc.date.issued2023-11-
dc.identifier.isbn978-989-758-674-3de_CH
dc.identifier.issn2184-3236de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29214-
dc.description.abstractAs 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.de_CH
dc.language.isoende_CH
dc.publisherSciTePressde_CH
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subjectDeep learningde_CH
dc.subjectRobustnessde_CH
dc.subjectSignal detectionde_CH
dc.subjectUnmanned aerial vehiclede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleRobust drone detection and classification from radio frequency signals using convolutional neural networksde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.publisher.placeSetubalde_CH
dc.identifier.doi10.5220/0012176800003595de_CH
dc.identifier.doi10.21256/zhaw-29214-
zhaw.conference.details15th International Joint Conference on Computational Intelligence (IJCCI), Rome, Italy, 13-15 November 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end504de_CH
zhaw.pages.start496de_CH
zhaw.parentwork.editorvan Stein, Niki-
zhaw.parentwork.editorMarcelloni, Francesco-
zhaw.parentwork.editorLam, H.K.-
zhaw.parentwork.editorFilipe, Joaquim-
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 15th International Joint Conference on Computational Intelligence - NCTAde_CH
zhaw.webfeedDatalabde_CH
zhaw.funding.zhawDrone Signal Datasetde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
zhaw.relation.referenceshttps://www.kaggle.com/datasets/sgluege/noisy-drone-rf-signal-classificationde_CH
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Glüge, S., Nyfeler, M., Ramagnano, N., Horn, C., & Schüpbach, C. (2023). Robust drone detection and classification from radio frequency signals using convolutional neural networks [Conference paper]. In N. van Stein, F. Marcelloni, H. K. Lam, & J. Filipe (Eds.), Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA (pp. 496–504). SciTePress. https://doi.org/10.5220/0012176800003595
Glüge, S. et al. (2023) ‘Robust drone detection and classification from radio frequency signals using convolutional neural networks’, in N. van Stein et al. (eds) Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA. Setubal: SciTePress, pp. 496–504. Available at: https://doi.org/10.5220/0012176800003595.
S. Glüge, M. Nyfeler, N. Ramagnano, C. Horn, and C. Schüpbach, “Robust drone detection and classification from radio frequency signals using convolutional neural networks,” in Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA, Nov. 2023, pp. 496–504. doi: 10.5220/0012176800003595.
GLÜGE, Stefan, Matthias NYFELER, Nicola RAMAGNANO, Claus HORN und Christof SCHÜPBACH, 2023. Robust drone detection and classification from radio frequency signals using convolutional neural networks. In: Niki VAN STEIN, Francesco MARCELLONI, H.K. LAM und Joaquim FILIPE (Hrsg.), Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA. Conference paper. Setubal: SciTePress. November 2023. S. 496–504. ISBN 978-989-758-674-3
Glüge, Stefan, Matthias Nyfeler, Nicola Ramagnano, Claus Horn, and Christof Schüpbach. 2023. “Robust Drone Detection and Classification from Radio Frequency Signals Using Convolutional Neural Networks.” Conference paper. In Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA, edited by Niki van Stein, Francesco Marcelloni, H.K. Lam, and Joaquim Filipe, 496–504. Setubal: SciTePress. https://doi.org/10.5220/0012176800003595.
Glüge, Stefan, et al. “Robust Drone Detection and Classification from Radio Frequency Signals Using Convolutional Neural Networks.” Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA, edited by Niki van Stein et al., SciTePress, 2023, pp. 496–504, https://doi.org/10.5220/0012176800003595.


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