Analysis of Artificial Intelligence Methods for Detecting Drones Based on Radio Frequency Activity

Basan, Elena; Tregubenko, Maxim D.; Mudruk, Nikita N.; Abramov, Evgeny

doi:10.1109/apeie52976.2021.9647562

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…In [15], the authors who constructed the drone dataset [32] classified the data into four classes (3 drones and 1 background) with an accuracy of 85.4% by using the frequency spectrum and DNN. Moreover, various studies [33]- [35] have used 1DCNN and fully connected neural networks to frequency data, and obtained significantly good accuracy results such as 85.8% [33], 92.02% [34], and 92.5% [35], respectively. [16] achieved 95.6% accuracy by using multichannel CNN as a classifier.…”

Section: Radio Frequency-based Methodsmentioning

confidence: 99%

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

et al. 2022

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Since the beginning of the COVID-19 pandemic, the demand for unmanned aerial vehicles (UAVs) has surged owing to an increasing requirement of remote, noncontact, and technologically advanced interactions. However, with the increased demand for drones across a wide range of fields, their malicious use has also increased. Therefore, an anti-UAV system is required to detect unauthorized drone use. In this study, we propose a radio frequency (RF) based solution that uses 15 drone controller signals. The proposed method can solve the problems associated with the RF based detection method, which has poor classification accuracy when the distance between the controller and antenna increases or the signal-to-noise ratio (SNR) decreases owing to the presence of a large amount of noise. For the experiment, we changed the SNR of the controller signal by adding white Gaussian noise to SNRs of -15 to 15 dB at 5 dB intervals. A power-based spectrogram image with an applied threshold value was used for convolution neural network training. The proposed model achieved 98% accuracy at an SNR of -15 dB and 99.17% accuracy in the classification of 105 classes with 15 drone controllers within 7 SNR regions. From these results, it was confirmed that the proposed method is both noise-tolerant and scalable. INDEX TERMSAnti-drone systems, convolutional neural networks (CNNs), noise-tolerant, spectrograms, unmanned aerial vehicles (UAVs), UAV classification I. INTRODUCTION U NMANNED aerial vehicles (UAVs), including drones, are used for various purposes, such as delivery, agriculture, transportation, and communication [1]. The potential uses of such vehicles are continually increasing [2]. Additionally, social and commercial demands for remote technology have increased owing to the recent COVID-19 outbreak, and drones are being proposed as a noncontact solution for numerous applications. The approach in [3] shows how backup transportation systems based on existing drone infrastructure can play an important role during the COVID-19 pandemic and similar situations. Additionally, the authors in [4] proposed the application of drones for spraying disinfectants to combat the COVID-19 pandemic. However,

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Section: Radio Frequency-based Methodsmentioning

confidence: 99%

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

et al. 2022

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“…Training (60%) + Cross-validation (20%) and 20% for Testing). [49] DroneRF datasets (composed of 227 recorded segments collected from 3 different drones, size of datasets 3.75 GB) [57], containing 22700 elements and 2047 features and training, validation, and testing ratio 70%, 10%, and 20%. [50] Used own datasets with seven DJI drones; 500 spectrograms are generated; 90% used for training and 10% for validation.…”

Section: Uav Classification Based On ML Using Rf Analysismentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

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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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Analysis of Artificial Intelligence Methods for Detecting Drones Based on Radio Frequency Activity

Cited by 5 publications

References 5 publications

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

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

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