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DroneRF dataset: A dataset of drones for RF-based detection, classification and identification

Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put...

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Autores principales: Allahham, MHD Saria, Al-Sa'd, Mohammad F., Al-Ali, Abdulla, Mohamed, Amr, Khattab, Tamer, Erbad, Aiman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6727013/
https://www.ncbi.nlm.nih.gov/pubmed/31508463
http://dx.doi.org/10.1016/j.dib.2019.104313
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author Allahham, MHD Saria
Al-Sa'd, Mohammad F.
Al-Ali, Abdulla
Mohamed, Amr
Khattab, Tamer
Erbad, Aiman
author_facet Allahham, MHD Saria
Al-Sa'd, Mohammad F.
Al-Ali, Abdulla
Mohamed, Amr
Khattab, Tamer
Erbad, Aiman
author_sort Allahham, MHD Saria
collection PubMed
description Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put, have become increasingly valuable commodities. This article describes the DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone's communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. An example of how this dataset can be interpreted and used can be found in the related research article “RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database” (Al-Sa'd et al., 2019).
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spelling pubmed-67270132019-09-10 DroneRF dataset: A dataset of drones for RF-based detection, classification and identification Allahham, MHD Saria Al-Sa'd, Mohammad F. Al-Ali, Abdulla Mohamed, Amr Khattab, Tamer Erbad, Aiman Data Brief Engineering Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put, have become increasingly valuable commodities. This article describes the DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone's communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. An example of how this dataset can be interpreted and used can be found in the related research article “RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database” (Al-Sa'd et al., 2019). Elsevier 2019-08-26 /pmc/articles/PMC6727013/ /pubmed/31508463 http://dx.doi.org/10.1016/j.dib.2019.104313 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Engineering
Allahham, MHD Saria
Al-Sa'd, Mohammad F.
Al-Ali, Abdulla
Mohamed, Amr
Khattab, Tamer
Erbad, Aiman
DroneRF dataset: A dataset of drones for RF-based detection, classification and identification
title DroneRF dataset: A dataset of drones for RF-based detection, classification and identification
title_full DroneRF dataset: A dataset of drones for RF-based detection, classification and identification
title_fullStr DroneRF dataset: A dataset of drones for RF-based detection, classification and identification
title_full_unstemmed DroneRF dataset: A dataset of drones for RF-based detection, classification and identification
title_short DroneRF dataset: A dataset of drones for RF-based detection, classification and identification
title_sort dronerf dataset: a dataset of drones for rf-based detection, classification and identification
topic Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6727013/
https://www.ncbi.nlm.nih.gov/pubmed/31508463
http://dx.doi.org/10.1016/j.dib.2019.104313
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