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

Allahham, Mhd Saria; Al-Sa'D, Mohamed Fathi; Al-Ali, Abdulla; Mohamed, Amr; Khattab, Tamer; Erbad, Aiman

doi:10.1016/j.dib.2019.104313

Cited by 92 publications

(93 citation statements)

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“…In certain cases, data collected in the laboratory, for example, are combined with a certain amount of noise to simulate a real environment. This research is based on a dataset that was generated based on a real experiment [ 34 ]. It consists from set of recorded raw samples and segments for each class with respect to different number of experiments.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…At last, the collected data is fetched, stored and processed using a laptop or any other interfacing device equipped with LabVIEW software. In this research, we will use a public dataset, where the collected data is based on multiple experiments as discussed in Reference [34]. The first component in the system model is the UAV, authors used three types of drones/UAVs: Parrot Bebop, Parrot AR Drone, and DJI Phantom 3.…”

Section: System Model and Uav Detection Problemmentioning

confidence: 99%

“…The collected RF data at the receiver side is usually used for training and testing the model. The recorded time for the RF background activities is around 10.25 s, and the RF UAV communication for each flight mode is roughly around 5.25 s. The used dataset in validating our approach was published in 2019 [34], and the recorded segments were collected based on three different levels as illustrated below: In order to prevent memory overload, the collected RF-signals were stored as segments in a CSV-format, based on the authors in Reference [34]. This makes loading and interpreting of the RF-UAV dataset using any compatible application simple and even easier than other formats.…”

Section: Datasetmentioning

confidence: 99%

“…This dataset has been used in previous research studies [26,28,32], and the distribution of samples for all cases is as follows: Based on the above description of the dataset [34], the detection problem can be solved as a multi class problem in a hierarchical manner. From our understanding, the system will be built based on four classifiers to identify the presence of the UAV, type of the UAV, and the mode of the UAV as discussed in the next section.…”

Section: Datasetmentioning

confidence: 99%

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RF-Based UAV Detection and Identification Using Hierarchical Learning Approach

Nemer

Sheltami

Ahmad

et al. 2021

Sensors

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Unmanned Aerial Vehicles (UAVs) are widely available in the current market to be used either for recreation as a hobby or to serve specific industrial requirements, such as agriculture and construction. However, illegitimate and criminal usage of UAVs is also on the rise which introduces their effective identification and detection as a research challenge. This paper proposes a novel machine learning-based for efficient identification and detection of UAVs. Specifically, an improved UAV identification and detection approach is presented using an ensemble learning based on the hierarchical concept, along with pre-processing and feature extraction stages for the Radio Frequency (RF) data. Filtering is applied on the RF signals in the detection approach to improve the output. This approach consists of four classifiers and they are working in a hierarchical way. The sample will pass the first classifier to check the availability of the UAV, and then it will specify the type of the detected UAV using the second classifier. The last two classifiers will handle the sample that is related to Bebop and AR to specify their mode. Evaluation of the proposed approach with publicly available dataset demonstrates better efficiency compared to existing detection systems in the literature. It has the ability to investigate whether a UAV is flying within the area or not, and it can directly identify the type of UAV and then the flight mode of the detected UAV with accuracy around 99%.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: System Model and Uav Detection Problemmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

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RF-Based UAV Detection and Identification Using Hierarchical Learning Approach

Nemer

Sheltami

Ahmad

et al. 2021

Sensors

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Deep learning approach for investigation of temporal radio frequency signatures of drones

Sohal¹,

Grewal²,

Singh³

et al. 2022

Int J Communication

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The omnipresence of drones in the civilian air space has led to their malicious usage raising high alert security issues. In this paper, a deep learning approach to detect and identify drones and to determine their flight modes from the remotely sensed radio frequency (RF) signatures is presented. This work intends to detect the presence of drones using two-class classification, the presence along with identification of their make using four-class classification.And this is further extended to the determination of their flight modes using ten-class classification. It employs the proposed architectures of prominent deep learning classifiers, namely, autoencoder (AE), long short-term memory (LSTM), convolutional neural network (CNN), and CNN-LSTM hybrid model. To procure the relevant information from 227 RF signatures having 100 fragments each, the seven significant temporal statistical features, namely, maxima, minima, mean, variance, skewness, kurtosis, and root mean square, are extracted. In a two-class classification scenario, all considered classifiers perform near to idle, whereas in a four-class classification scenario, CNN performs best, followed by AE, CNN-LSTM, and LSTM, respectively. Moreover, in a ten-class classification scenario, AE far outperforms CNN, followed by LSTM and CNN-LSTM, respectively. The best performance in terms of accuracy and classification time confirms the feasibility of the proposed AE classifier for the three considered drone operations.

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