RF-Based Drone Detection with Deep Neural Network: Review and Case Study

Norah A. Almubairik; El Sayed M. El-Alfy

doi:10.1007/978-981-99-8184-7_2

RF-Based Drone Detection with Deep Neural Network: Review and Case Study

Norah A. Almubairik, El Sayed M. El-Alfy^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings
Editors	Biao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	16-27
Number of pages	12
ISBN (Print)	9789819981830
DOIs	https://doi.org/10.1007/978-981-99-8184-7_2
State	Published - 2024
Event	30th International Conference on Neural Information Processing, ICONIP 2023 - Changsha, China Duration: 20 Nov 2023 → 23 Nov 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1969 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	30th International Conference on Neural Information Processing, ICONIP 2023
Country/Territory	China
City	Changsha
Period	20/11/23 → 23/11/23

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Border security
Deep learning
Drone detection
FFT spectrum
Radio-frequency signals

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-981-99-8184-7_2

Cite this

Almubairik, N. A., & El-Alfy, E. S. M. (2024). RF-Based Drone Detection with Deep Neural Network: Review and Case Study. In B. Luo, L. Cheng, Z.-G. Wu, H. Li, & C. Li (Eds.), Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings (pp. 16-27). (Communications in Computer and Information Science; Vol. 1969 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8184-7_2

Almubairik, Norah A. ; El-Alfy, El Sayed M. / RF-Based Drone Detection with Deep Neural Network : Review and Case Study. Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. editor / Biao Luo ; Long Cheng ; Zheng-Guang Wu ; Hongyi Li ; Chaojie Li. Springer Science and Business Media Deutschland GmbH, 2024. pp. 16-27 (Communications in Computer and Information Science).

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abstract = "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.",

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Almubairik, NA & El-Alfy, ESM 2024, RF-Based Drone Detection with Deep Neural Network: Review and Case Study. in B Luo, L Cheng, Z-G Wu, H Li & C Li (eds), Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. Communications in Computer and Information Science, vol. 1969 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 16-27, 30th International Conference on Neural Information Processing, ICONIP 2023, Changsha, China, 20/11/23. https://doi.org/10.1007/978-981-99-8184-7_2

RF-Based Drone Detection with Deep Neural Network: Review and Case Study. / Almubairik, Norah A.; El-Alfy, El Sayed M.
Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. ed. / Biao Luo; Long Cheng; Zheng-Guang Wu; Hongyi Li; Chaojie Li. Springer Science and Business Media Deutschland GmbH, 2024. p. 16-27 (Communications in Computer and Information Science; Vol. 1969 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

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Almubairik NA, El-Alfy ESM. RF-Based Drone Detection with Deep Neural Network: Review and Case Study. In Luo B, Cheng L, Wu ZG, Li H, Li C, editors, Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 16-27. (Communications in Computer and Information Science). doi: 10.1007/978-981-99-8184-7_2