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  2. Life Sciences und Facility Management
  3. Forschungsdaten
  4. ZHAW Forschungsdaten Life Sciences and Facility Management
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dc.contributor.authorGlüge, Stefan-
dc.contributor.authorNyfeler, Matthias-
dc.contributor.authorRamagnano, Nicola-
dc.contributor.authorHorn, Claus-
dc.contributor.authorSchüpbach, Christoph-
dc.date.accessioned2023-12-01T16:40:51Z-
dc.date.available2023-12-01T16:40:51Z-
dc.date.issued2023-06-
dc.identifier.urihttps://www.kaggle.com/datasets/sgluege/noisy-drone-rf-signal-classificationde_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29276-
dc.description.abstractWe provide an easy-to-use benchmark dataset to enable model development for the detection/classification of drone signals. It consists of the non-overlapping signal vectors of length of 16384, which corresponds to approx. 1.2ms at 14MHz. We have also added Labnoise (Bluetooth, Wi-Fi, Amplifier) and Gaussian noise to the dataset. After normalization, the drone signals were mixed with either Labnoise (50%) or Gaussian noise (50%). The noise class was created by mixing Labnoise and Gaussian noise in all possible combinations (i.e., Labnoise + Labnoise, Labnoise + Gaussian noise, Gaussian noise + Labnoise, and Gaussian noise + Gaussian noise). For the drone signal classes, as for the noise class, the number of samples for each level of SNR is equally distributed over the interval of SNR in [-20, 30]dB in steps of 2dB, i.e., 3792 - 3800 samples per SNR.de_CH
dc.language.isoende_CH
dc.publisherKagglede_CH
dc.relation.isreferencedbyhttps://digitalcollection.zhaw.ch/handle/11475/29214de_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectRobustnessde_CH
dc.subjectSignal detectionde_CH
dc.subjectUnmanned aerial vehiclede_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleNoisy drone RF signal classificationde_CH
dc.typeForschungsdatende_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.webfeedPredictive Analyticsde_CH
zhaw.funding.zhawDrone Signal Datasetde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:ZHAW Forschungsdaten Life Sciences and Facility Management

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Glüge, S., Nyfeler, M., Ramagnano, N., Horn, C., & Schüpbach, C. (2023). Noisy drone RF signal classification [Data set]. Kaggle. https://www.kaggle.com/datasets/sgluege/noisy-drone-rf-signal-classification
Glüge, S. et al. (2023) ‘Noisy drone RF signal classification’. Kaggle. Available at: https://www.kaggle.com/datasets/sgluege/noisy-drone-rf-signal-classification.
S. Glüge, M. Nyfeler, N. Ramagnano, C. Horn, and C. Schüpbach, “Noisy drone RF signal classification.” Kaggle, Jun. 2023. [Online]. Available: https://www.kaggle.com/datasets/sgluege/noisy-drone-rf-signal-classification
GLÜGE, Stefan, Matthias NYFELER, Nicola RAMAGNANO, Claus HORN und Christoph SCHÜPBACH, 2023. Noisy drone RF signal classification [online]. Data set. Juni 2023. Kaggle. Verfügbar unter: https://www.kaggle.com/datasets/sgluege/noisy-drone-rf-signal-classification
Glüge, Stefan, Matthias Nyfeler, Nicola Ramagnano, Claus Horn, and Christoph Schüpbach. 2023. “Noisy Drone RF Signal Classification.” Data set. Kaggle. https://www.kaggle.com/datasets/sgluege/noisy-drone-rf-signal-classification.
Glüge, Stefan, et al. Noisy Drone RF Signal Classification. Kaggle, June 2023, https://www.kaggle.com/datasets/sgluege/noisy-drone-rf-signal-classification.


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