TY - GEN
T1 - RF-Based Drone Detection with Deep Neural Network
T2 - 30th International Conference on Neural Information Processing, ICONIP 2023
AU - Almubairik, Norah A.
AU - El-Alfy, El Sayed M.
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2024
Y1 - 2024
N2 - Drones have been widely used in many application scenarios, such as logistics and on-demand instant delivery, surveillance, traffic monitoring, firefighting, photography, and recreation. On the other hand, there is a growing level of misemployment and malicious utilization of drones being reported on a local and global scale. Thus, it is essential to employ security measures to reduce these risks. Drone detection is a crucial initial step in several tasks such as identifying, locating, tracking, and intercepting malicious drones. This paper reviews related work for drone detection and classification based on deep neural networks. Moreover, it presents a case study to compare the impact of utilizing magnitude and phase spectra as input to the classifier. The results indicate that prediction performance is better when the magnitude spectrum is used. However, the phase spectrum can be more resilient to errors due to signal attenuation and changes in the surrounding conditions.
AB - Drones have been widely used in many application scenarios, such as logistics and on-demand instant delivery, surveillance, traffic monitoring, firefighting, photography, and recreation. On the other hand, there is a growing level of misemployment and malicious utilization of drones being reported on a local and global scale. Thus, it is essential to employ security measures to reduce these risks. Drone detection is a crucial initial step in several tasks such as identifying, locating, tracking, and intercepting malicious drones. This paper reviews related work for drone detection and classification based on deep neural networks. Moreover, it presents a case study to compare the impact of utilizing magnitude and phase spectra as input to the classifier. The results indicate that prediction performance is better when the magnitude spectrum is used. However, the phase spectrum can be more resilient to errors due to signal attenuation and changes in the surrounding conditions.
KW - Border security
KW - Deep learning
KW - Drone detection
KW - FFT spectrum
KW - Radio-frequency signals
UR - https://www.scopus.com/pages/publications/85178627768
U2 - 10.1007/978-981-99-8184-7_2
DO - 10.1007/978-981-99-8184-7_2
M3 - Conference contribution
AN - SCOPUS:85178627768
SN - 9789819981830
T3 - Communications in Computer and Information Science
SP - 16
EP - 27
BT - Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings
A2 - Luo, Biao
A2 - Cheng, Long
A2 - Wu, Zheng-Guang
A2 - Li, Hongyi
A2 - Li, Chaojie
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 20 November 2023 through 23 November 2023
ER -