Page 63 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 5 – Internet of Everything

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 5

[14] V. Thai, W. Zhong, T. Pham, S. Alam, and V. Duong. [25] N. Regev, I. Yoffe, and D. Wulich. “Classi ication of
“Detection, Tracking and Classi ication of Aircraft single and multi propelled miniature drones using
and Drones in Digital Towers Using Machine Learn‑ multilayer perceptron arti icial neural network”.
ing on Motion Patterns”. In: Proc. Integrated Com‑ In: Proc. Int. Conf. Radar Syst. Belfast. UK, Jan. 2017,
mun. Navig. Surveillance Conf. (ICNS). Herndon, VA, pp. 1–5.
Apr. 2019, pp. 1–8. [26] M. Ezuma, F. Erden, C. Kumar Anjinappa, O.
[15] M. Saqib, S. Daud Khan, N. Sharma, and M. Blumen‑ Ozdemir, and I. Guvenc. “Detection and Classi ica‑
stein. “A study on detecting drones using deep con‑ tion of UAVs Using RF Fingerprints in the Presence
volutional neural networks”. In: Proc. IEEE Int. Conf. of Wi‑Fi and Bluetooth Interference”. In: IEEE Open
Advanced Video Signal Based Surveillance (AVSS). J. Commun. Soc. 1 (Nov. 2019), pp. 60–76.
Lecce, Italy, Aug. 2017, pp. 1–5.
[27] Martins Ezuma; Fatih Erden; Chethan K. Anji‑
[16] J. Ren and X. Jiang. “Regularized 2‑D Complex‑Log nappa; Ozgur Ozdemir; Ismail Guvenc. “Drone Re‑
Spectral Analysis and Subspace Reliability Analysis mote Controller RF Signal Dataset”. In: IEEE Dat‑
of Micro‑Doppler Signature for UAV Detection”. In: aport, 2020. DOI: 10 . 21227 / ss99 - 8d56. URL:
Pattern Recognit. 69 (Mar. 2017), pp. 225–237. https://dx.doi.org/10.21227/ss99-8d56.
[17] M. Marco and G. Pinelli. “Classi ication of Drones [28] Z. Shi, X. Chang, C. Yang, Z. Wu, and J. Wu. “An
with a Surveillance Radar Signal”. In: Proc. Int. Conf. Acoustic‑Based Surveillance System for Amateur
Comput Vision Syst. Thessaloniki, Greece, Sept. Drones Detection and Localization”. In: IEEE Trans.
2019, pp. 723–733. Veh. Technol. 69.3 (Mar. 2020), pp. 2731–2739.
[18] P. Zhang, L. Yang, G. Chen, and G. Li. “Classi ication [29] C. Aker and S. Kalkan. “Using deep networks for
of drones based on micro‑Doppler signatures with drone detection”. In: Proc. IEEE Int. Conf. Advanced
dual‑band radar sensors”. In: Proc. Progress Elec‑ Video Signal Based Surveillance (AVSS). Lecce, Italy,
tromagn. Research Symp. (PIERS). Singapore, Singa‑ Aug. 2017, pp. 1–6.
pore, Nov. 2017, pp. 638–643. [30] Y. Zhao and Y. Su. “The Extraction of Micro‑Doppler
[19] A. Huizing, M. Heiligers, B. Dekker, J. de Wit, Signal With EMD Algorithm for Radar‑Based Small
L. Cifola, and R. Harmanny. “Deep Learning for UAVs’ Detection”. In: IEEE Trans. Instrum. Meas.
Classi ication of Mini‑UAVs Using Micro‑Doppler 69.3 (Apr. 2020), pp. 929–940.
Spectrograms in Cognitive Radar”. In: IEEE Trans. [31] B. Choi and D. Oh. “Classi ication of Drone Type
Aerosp. Electron. Syst. 34.11 (Nov. 2019), pp. 46–56.
Using Deep Convolutional Neural Networks Based
[20] B. K. Kim, H. Kang, and S. Park. “Drone Classi i‑ on Micro‑Doppler Simulation”. In: Proc. Int. Symp.
cation Using Convolutional Neural Networks With Antennas Propag. (ISAP). Busan, South Korea, Oct.
Merged Doppler Images”. In: IEEE Geosci. Remote 2018, pp. 1–2.
Sens. Lett. 14.1 (Jan. 2017), pp. 38–42.
[32] A. Shoufan, H. M. Al‑Angari, M. F. A. Sheikh, and E.
[21] B. Oh, X. Guo, F. Wan, K. Toh, and Z. Lin. “Micro‑ Damiani. “Drone Pilot Identi ication by Classifying
Doppler Mini‑UAV Classi ication Using Empirical‑ Radio‑Control Signals”. In: IEEE Trans. Inf. Forensics
Mode Decomposition Features”. In: IEEE Geosci. Re‑ Security 13.10 (Mar. 2018), pp. 2439–2447.
mote Sens. Lett. 15.2 (Feb. 2018), pp. 227–231. [33] Roberto Brunelli. Template Matching Techniques in
[22] P. Molchanov, K. Egiazarian, J. Astola, R. I. A. Har‑ Computer Vision: Theory and Practice. John Wiley &
manny, and J. J. M. de Wit. “Classi ication of small Sons, Ltd, 2009. ISBN: 9780470744055.
UAVs and birds by micro‑Doppler signatures”. In: [34] P. Sysel and Z. Smékal. “Enhanced estimation of
Proc. Eur. Radar Conf. Nuremberg, Germany, Oct. power spectral density of noise using the wavelet
2013, pp. 172–175. transform”. In: Pers. Wireless Commun. Boston, MA:
[23] L. Wang, J. Tang, and Q. Liao. “A Study on Radar Tar‑ Springer US, 2007, pp. 521–532. ISBN: 978‑0‑387‑
get Detection Based on Deep Neural Networks”. In: 74159‑8.
IEEE Sens. Lett. 3.3 (Jan. 2019), pp. 1–4. [35] Francisco‑Cervantes Cervantes, Jesus Gonzalez‑
[24] A. Alipour‑Fanid, M. Dabaghchian, N. Wang, P. Trejo, Cesar Real‑Ramirez, and Hoyos‑Reyes Luis.
Wang, L. Zhao, and K. Zeng. “Machine Learning‑ “Fractal dimension algorithms and their applica‑
Based Delay‑Aware UAV Detection and Operation tion to time series associated with natural phe‑
Mode Identi ication Over Encrypted Wi‑Fi Traf‑ nomena”. In: J. Phys.: Conf. Ser. 475 (Nov. 2013).
ic”. In: IEEE Trans. Inf. Forensics Security 15 (Dec.
[36] Aurélien Géron. Hands‑On Machine Learning with
2019), pp. 2346–2360.
Scikit‑Learn, Keras, and TensorFlow: Concepts,
Tools, and Techniques to Build Intelligent Systems.
O’Reilly Media, 2019.

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