Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification
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 us...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9998502/ |
| _version_ | 1797974418594463744 |
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| author | Dae-Il Noh Seon-Geun Jeong Huu-Trung Hoang Quoc-Viet Pham Thien Huynh-The Mikio Hasegawa Hiroo Sekiya Sun-Young Kwon Sang-Hwa Chung Won-Joo Hwang |
| author_facet | Dae-Il Noh Seon-Geun Jeong Huu-Trung Hoang Quoc-Viet Pham Thien Huynh-The Mikio Hasegawa Hiroo Sekiya Sun-Young Kwon Sang-Hwa Chung Won-Joo Hwang |
| author_sort | Dae-Il Noh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T04:19:37Z |
| format | Article |
| id | doaj.art-845dab1a06fe477488abb9f52c719508 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T04:19:37Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-845dab1a06fe477488abb9f52c7195082022-12-31T00:00:58ZengIEEEIEEE Access2169-35362022-01-011013478513479810.1109/ACCESS.2022.32320369998502Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal ClassificationDae-Il Noh0https://orcid.org/0000-0002-6586-5780Seon-Geun Jeong1https://orcid.org/0000-0002-5926-6642Huu-Trung Hoang2https://orcid.org/0000-0002-1031-9635Quoc-Viet Pham3https://orcid.org/0000-0002-9485-9216Thien Huynh-The4https://orcid.org/0000-0002-9172-2935Mikio Hasegawa5https://orcid.org/0000-0001-5638-8022Hiroo Sekiya6https://orcid.org/0000-0003-3557-1463Sun-Young Kwon7https://orcid.org/0000-0003-3433-1409Sang-Hwa Chung8https://orcid.org/0000-0003-1329-1188Won-Joo Hwang9https://orcid.org/0000-0001-8398-564XDepartment of Information Convergence Engineering, Pusan National University, Busan, South KoreaDepartment of Information Convergence Engineering, Pusan National University, Busan, South KoreaUniversity of Economics, Hue University, Hue, VietnamKorean Southeast Center for the 4th Industrial Revolution Leader Education, Pusan National University, Busan, South KoreaFaculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, VietnamDepartment of Electrical Engineering, Tokyo University of Science, Tokyo, JapanGraduate School of Engineering, Chiba University, Chiba, JapanDepartment of Information Convergence Engineering, Pusan National University, Busan, South KoreaDepartment of Information Convergence Engineering, Pusan National University, Busan, South KoreaDepartment of Information Convergence Engineering, Pusan National University, Busan, South KoreaSince 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.https://ieeexplore.ieee.org/document/9998502/Anti-drone systemsconvolutional neural networks (CNNs)noise-tolerantspectrogramsunmanned aerial vehicles (UAVs)UAV classification |
| spellingShingle | Dae-Il Noh Seon-Geun Jeong Huu-Trung Hoang Quoc-Viet Pham Thien Huynh-The Mikio Hasegawa Hiroo Sekiya Sun-Young Kwon Sang-Hwa Chung Won-Joo Hwang Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification IEEE Access Anti-drone systems convolutional neural networks (CNNs) noise-tolerant spectrograms unmanned aerial vehicles (UAVs) UAV classification |
| title | Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification |
| title_full | Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification |
| title_fullStr | Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification |
| title_full_unstemmed | Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification |
| title_short | Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification |
| title_sort | signal preprocessing technique with noise tolerant for rf based uav signal classification |
| topic | Anti-drone systems convolutional neural networks (CNNs) noise-tolerant spectrograms unmanned aerial vehicles (UAVs) UAV classification |
| url | https://ieeexplore.ieee.org/document/9998502/ |
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