| Publication type: | Research data |
| Title: | Noisy drone RF signal classification |
| Authors: | Glüge, Stefan Nyfeler, Matthias Ramagnano, Nicola Horn, Claus Schüpbach, Christoph |
| et. al: | No |
| Issue Date: | Jun-2023 |
| Publisher / Ed. Institution: | Kaggle |
| Language: | English |
| Subjects: | Robustness; Signal detection; Unmanned aerial vehicle |
| Subject (DDC): | 005: Computer programming, programs and data |
| Abstract: | We 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. |
| URI: | https://www.kaggle.com/datasets/sgluege/noisy-drone-rf-signal-classification https://digitalcollection.zhaw.ch/handle/11475/29276 |
| Related publications: | https://digitalcollection.zhaw.ch/handle/11475/29214 |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Computational Life Sciences (ICLS) |
| Published as part of the ZHAW project: | Drone Signal Dataset |
| 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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