Machine Learning Framework for RF-Based Drone Detection and Identification System

Medaiyese, Olusiji O; Syed, Abbas; Lauf, Adrian P.

doi:10.1109/icon-sonics53103.2021.9617168

Cited by 33 publications

(19 citation statements)

References 13 publications

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“…For example, the GTCC shows an improvement of 3.3% compared to WPD for the DNN classifier. Our results have been compared to the results shown in five relevant references that used the same dataset [27][28][29][30][31] for different classifiers. The first three references utilize only the PSD features, while [31] uses both PSD and MFCC, and [30] uses continuous wavelet transform.…”

Section: Drone Rf Datasetsmentioning

confidence: 99%

“…Medaiyese et al [ 29 ] propose a machine learning-based system for drone detection and identification, which uses low band RF signals from drone to flight-controller communication. Three machine learning models were developed using the XGBoost algorithm to detect and identify the presence of a drone, the type of drone and the operational mode of the detected drone.…”

Section: Related Workmentioning

confidence: 99%

“…Similar works based on the radar for drone classification in different radar systems (1)(2)(3)(4) GHz) can achieve a high accuracy of 95-100% using machine learning methods [22][23][24]. Currently, the leading drone detection techniques are based on either RF or acoustic signals [15,[27][28][29][30][31][32][33][34][35][36][37]. Therefore, the following literature review focuses on related work that is based on these two approaches.…”

Section: Introductionmentioning

confidence: 99%

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Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks

Frid,

Ben-Shimol,

Manor

et al. 2024

Sensors

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The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the entertainment industry, and more. However, misuse of drone technology can potentially lead to military threats, terrorist acts, as well as privacy and safety breaches. This emphasizes the need for effective and fast remote detection of potentially threatening drones. In this study, we propose a novel approach for automatic drone detection utilizing the usage of both radio frequency communication signals and acoustic signals derived from UAV rotor sounds. In particular, we propose the use of classical and deep machine-learning techniques and the fusion of RF and acoustic features for efficient and accurate drone classification. Distinct types of ML-based classifiers have been examined, including CNN- and RNN-based networks and the classical SVM method. The proposed approach has been evaluated with both frequency and audio features using common drone datasets, demonstrating better accuracy than existing state-of-the-art methods, especially in low SNR scenarios. The results presented in this paper show a classification accuracy of approximately 91% at an SNR ratio of −10 dB using the LSTM network and fused features.

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Section: Drone Rf Datasetsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks

Frid,

Ben-Shimol,

Manor

et al. 2024

Sensors

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“…In recent years, many research works have been published to address UAV detection, tracking, and classification problems. The main drone detection technologies are: radar sensors [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], RF sensors [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], audio sensors [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], and camera sensors using visual UAV characteristics [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Based on the above-mentioned sources, the advantages and disadvantages of each drone detection technology are compared in Table 2 .…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…Recent related studies on the use of RF sensors for UAV detection and classification may be found in [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ].…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

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The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.

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Inception Resnet V2-ECANet Based on Gramian Angular Field Image for Specific Emitter Identification

Ma¹,

Wu²,

Zhong³

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Machine Learning Framework for RF-Based Drone Detection and Identification System

Cited by 33 publications

References 13 publications

Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks

Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Inception Resnet V2-ECANet Based on Gramian Angular Field Image for Specific Emitter Identification

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