RF-based drone detection and classification system using convolutional neural network

Mokhtari, Mohammed; Bajčetić, Jovan; Sazdic-Jotic, Boban; Pavlović, Boban

doi:10.1109/telfor52709.2021.9653332

Cited by 6 publications

(3 citation statements)

References 8 publications

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Section: Radio Frequency-based Methodsmentioning

confidence: 99%

“…To obtain higher classification accuracy, studies using the spectrogram have been recently proposed [28]- [30], [37]. The authors of [37] shared the dataset with [38], and obtained 100% accuracy by using the RGB values of the spectrogram and a CNN classifier. In [28], 99.9 % accuracy was obtained by extracting features from the PSD, which is the spectrogram value, through CNN transfer learning and classifying them using logistic regression.…”

Section: Radio Frequency-based Methodsmentioning

confidence: 99%

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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%

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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“…While using RF ground communication signals for drone detection has limitations, it offers a cost-efficient and easy-to-implement mechanism [44,[110][111][112][113][114]116]. It can be useful when operating over a more extended area and period of time.…”

Section: Rf Ground Communication Sensorsmentioning

confidence: 99%

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

Famili,

Stavrou,

Wang

et al. 2024

Sensors

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Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.

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Application of SNNS Model Based On Multi-Dimensional Attention In Drone Radio Frequency Signal Classification

Si,

Liu,

Liu

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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RF-based drone detection and classification system using convolutional neural network

Cited by 6 publications

References 8 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

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

Application of SNNS Model Based On Multi-Dimensional Attention In Drone Radio Frequency Signal Classification

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