Deep Learning for RF-Based Drone Detection and Identification: A Multi-Channel 1-D Convolutional Neural Networks Approach

Allahham, Mhd Saria; Khattab, Tamer; Mohamed, Amr

doi:10.1109/iciot48696.2020.9089657

Cited by 60 publications

(33 citation statements)

References 20 publications

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“…The proposed method could be extended by other researches on UAV detection and classification performance improvement, including more effective feature extraction as well as novel classification models focusing on finer frequency details. [21] 46.8% 43.0% Classification method in [22] 59.2% 55.1% Classification method in [23] 87.4% /…”

Section: Discussionmentioning

confidence: 99%

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

Yang¹,

Luo²,

Wang³

et al. 2021

Preprint

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Section: Discussionmentioning

confidence: 99%

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

Yang¹,

Luo²,

Wang³

et al. 2021

Preprint

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“…The proposed model derives an accuracy of 59.2% and an F1 score of 55.1% for the ten-class classification. The multi-channel 1D CNN in [23] includes a feature extractor and a classical MLP.…”

Section: E Comparisonmentioning

confidence: 99%

“…The effectiveness of the method is verified based on a practical dataset in [21]. The experiment results show that the proposed approach achieves an accuracy of 98.4% and an F1 score of 98.3%, and outperforms other state-of-the-art methods [21] [22] [23].…”

Section: Introductionmentioning

confidence: 99%

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

Yang¹,

Luo²,

Wang³

et al. 2021

Preprint

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“…• T. Li, Z. Hong, Q. Cai, L.Yu, Z. Wen [25], [26]), or through camera-based target tracking from video streaming [27], [28] or statistically monitoring network traffic data [29], [30]. Acoustic-based approaches are typically sensitive to environmental noises whilst the visual quality of camera is subject to the surrounding conditions such as building blockage, ambient lighting, etc.…”

Section: Introductionmentioning

confidence: 99%

BisSiam: Bispectrum Siamese Network Based Contrastive Learning for UAV Anomaly Detection

Li¹,

Zhou²,

Cai³

et al. 2023

IEEE Trans. Knowl. Data Eng.

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In recent years, a surging number of unmanned aerial vehicles (UAVs) are pervasively utilized in many areas. However, the increasing number of UAVs may cause privacy and security issues such as voyeurism and espionage. It is critical for individuals or organizations to manage their behaviors and proactively prevent the misbehaved invasion of unauthorized UAVs through effective anomaly detection. The UAV anomaly detection framework needs to cope with complex signals in the noisy-prone environments and to function with very limited labeled samples. This paper proposes BISSIAM, a novel framework that is capable of identifying UAV presence, types and operation modes. BISSIAM converts UAVs signals to bispectrum as the input and exploits a siamese network based contrastive learning model to learn the vector encoding. A sampling mechanism is proposed for optimizing the sample size involved in the model training whilst ensuring the model accuracy without compromising the training efficiency. Finally, we present a similarity-based fingerprint matching mechanism for detecting unseen UAVs without the need of retraining the whole model. Experiment results show that our approach outperforms other baselines and can reach 92.85% accuracy of UAV type detection in unsupervised learning scenarios. 91.4% accuracy can be achieved when BISSIAM is used for detecting the UAV type of the out-of-sample UAVs.

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Deep Learning for RF-Based Drone Detection and Identification: A Multi-Channel 1-D Convolutional Neural Networks Approach

Cited by 60 publications

References 20 publications

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

BisSiam: Bispectrum Siamese Network Based Contrastive Learning for UAV Anomaly Detection

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