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Drone detection with radio frequency signals and deep learning models
- 1 Dalian University of Technology
* Author to whom correspondence should be addressed.
Abstract
The widespread use of drones raises security, environmental, privacy, and ethical issues; therefore, effective detection by drones is important. There are several methods for detecting drones, such as wireless signal detection, photoelectric detection, radar detection, and sound detection. However, these detection methods are not accurate enough to identify drones for use. To solve this question, more robust drone detection method are needed. In addition, for different types of drones and application scenarios, different technical means need to be used for detection and identification. Based on 2-class ,4-class and 10-class problems on an open ratio frequency (RF) signal dataset, we compared the drone detection and classification performances of different machine learning with deep learning models and multi-task models which is proposed by combining different RF methods with Convolutional neural networks (CNNs). Our experimental results show that the XGBoost model achieved the latest results on this groundbreaking dataset, with 99.96% accuracy for 2-class problem, 92.31% accuracy for 4-class problem, and 74.81% accuracy for 10-class problem, which exhibits the best performance for drone detection and classification.
Keywords
Deep learning, Machin learning, Drone detection, Radio Frequency Signal
[1]. Maamar Z, Kajan E, Asim M, et al. Open challenges in vetting the internet‐of‐things[J]. Internet Technology Letters, 2019, 2(5): e129.
[2]. Pugliese R, Regondi S, Marini R. Machine learning-based approach: Global trends, research directions, and regulatory standpoints[J]. Data Science and Management, 2021, 4: 19-29.
[3]. Zhao M, Zhang Y. GAN‐based deep neural networks for graph representation learning[J]. Engineering Reports, 2022, 4(11): e12517.
[4]. Chen X, Li H, Li C, et al. Single Image Dehazing Based on Sky Area Segmentation and Image Fusion[J]. IEICE TRANSACTIONS on Information and Systems, 2023, 106(7): 1249-1253.
[5]. Zheng Y, Jiang W. Evaluation of vision transformers for traffic sign classification[J]. Wireless Communications and Mobile Computing, 2022, 2022.
[6]. Jiang W. Cellular traffic prediction with machine learning: A survey[J]. Expert Systems with Applications, 2022, 201: 117163.
[7]. Jiang W. Graph-based deep learning for communication networks: A survey[J]. Computer Communications, 2022, 185: 40-54.
[8]. Dale H, Baker C, Antoniou M, et al. A Comparison of Convolutional Neural Networks for Low SNR Radar Classification of Drones[C]//2021 IEEE Radar Conference (RadarConf21). IEEE, 2021: 1-5.
[9]. Akter R, Doan V S, Lee J M, et al. CNN-SSDI: Convolution neural network inspired surveillance system for UAVs detection and identification[J]. Computer Networks, 2021, 201: 108519.
[10]. Raval D, Hunter E, Hudson S, et al. Convolutional Neural Networks for Classification of Drones Using Radars[J]. Drones, 2021, 5(4): 149.
[11]. Roychowdhury S, Ghosh D. Machine Learning Based Classification of Radar Signatures of Drones[C]//2021 2nd International Conference on Range Technology (ICORT). IEEE, 2021: 1-5.
[12]. Dadrass Javan F, Samadzadegan F, Gholamshahi M, et al. A Modified YOLOv4 Deep Learning Network for Vision-Based UAV Recognition[J]. Drones, 2022, 6(7): 160.
[13]. Kabir M S, Ndukwe I K, Awan E Z S. Deep Learning Inspired Vision based Frameworks for Drone Detection[C]//2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE, 2021: 1-5.
[14]. Samadzadegan F, Dadrass Javan F, Ashtari Mahini F, et al. Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery[J]. Aerospace, 2022, 9(1): 31.
[15]. Ajakwe S O, Ihekoronye V U, Kim D S, et al. DRONET: Multi-Tasking Framework for Real-Time Industrial Facility Aerial Surveillance and Safety[J]. Drones, 2022, 6(2): 46.
[16]. Fang J, Finn A, Wyber R, et al. Acoustic detection of unmanned aerial vehicles using biologically inspired vision processing[J]. The Journal of the Acoustical Society of America, 2022, 151(2): 968-981.
[17]. Casabianca P, Zhang Y. Acoustic-Based UAV Detection Using Late Fusion of Deep Neural Networks[J]. Drones, 2021, 5(3): 54.
[18]. Seo Y, Jang B, Im S. Drone detection using convolutional neural networks with acoustic stft features[C]//2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2018: 1-6.
[19]. Saqib M, Khan S D, Sharma N, et al. A study on detecting drones using deep convolutional neural networks[C]//2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2017: 1-5.
[20]. Kılıç R, Kumbasar N, Oral E A, et al. Drone classification using RF signal based spectral features[J]. Engineering Science and Technology, an International Journal, 2021.
[21]. Salman S, Mir J, Farooq M T, et al. Machine learning inspired efficient audio drone detection using acoustic features[C]//2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST). IEEE, 2021: 335-339.
[22]. Akter R, Doan V S, Zainudin A, et al. An Explainable Multi-Task Learning Approach for RF-based UAV Surveillance Systems[C]//2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2022: 145-149.
[23]. Jamil S, Rahman M U, Ullah A, et al. Malicious UAV detection using integrated audio and visual features for public safety applications[J]. Sensors, 2020, 20(14): 3923.
[24]. Svanström F, Englund C, Alonso-Fernandez F. Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors[C]//2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021: 7265-7272.
[25]. Jiang W. Software defined satellite networks: A survey[J]. Digital Communications and Networks, 2023.
[26]. Jiang W, He M, Gu W. Internet Traffic Prediction with Distributed Multi-Agent Learning[J]. Applied System Innovation, 2022, 5(6): 121.
[27]. Jiang W. PhD Forum Abstract: Crowd Sensing with Execution Uncertainty[C]//2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 2017: 251-252.
Cite this article
Dai,X. (2024). Drone detection with radio frequency signals and deep learning models. Applied and Computational Engineering,47,92-100.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
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