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All issues Volume 529 (2024) E3S Web of Conf., 529 (2024) 04005 References
Open Access
Issue
E3S Web of Conf.
Volume 529, 2024
International Conference on Sustainable Goals in Materials, Energy and Environment (ICSMEE’24)
Article Number 04005
Number of page(s) 13
Section Advanced Interdisciplinary Approaches
DOI https://doi.org/10.1051/e3sconf/202452904005
Published online 29 May 2024
  1. D. Sathyamoorthy, “A review of security threats of unmanned aerial vehicles and mitigation steps”, J. Defence Security, vol. 6, no. 1, pp. 81–97, (2015). [Google Scholar]
  2. B. Taha and A. Shoufan, “Machine learning-based drone detection and classification: State-of-the-art in research”, IEEE Access, vol. 7, pp. 138669–138682, (2019). [Google Scholar]
  3. G. Ding, Q. Wu, L. Zhang, Y. Lin, T. A. Tsiftsis, and Y.-D. Yao, “An amateur drone surveillance system based on the cognitive Internet of Things”, IEEE Commun. Mag., vol. 56, no. 1, pp. 29–35, Jan. (2018). [Google Scholar]
  4. F. Fioranelli, M. Ritchie, H. Griffiths, and H. Borrion, “Classification of loaded/unloaded micro-drones using multistatic radar”, Electron. Lett., vol. 51, no. 22, pp. 1813–1815, (2015). [Online]. [Google Scholar]
  5. J. Drozdowicz, et al., “35 GHz FMCW drone detection system”, in Proc. 17th Int. Radar Symp. (IRS), (2016), pp. 1–4. [Google Scholar]
  6. A. Coluccia, G. Parisi, and A. Fascista, “Detection and classification of multirotor drones in radar sensor networks: A review”, Sensors, vol. 20, no. 15, p. 4172, (2020). [Online]. [Google Scholar]
  7. Z. Zhang, Y. Cao, M. Ding, L. Zhuang, and W. Yao, “An intruder detection algorithm for vision-based sense and avoid system”, in Proc. Int. Conf. Unmanned Aircr. Syst. (ICUAS), (2016), pp. 550–556. [Google Scholar]
  8. S. R. Ganti and Y. Kim, “Implementation of detection and tracking mechanism for small uas”, in Proc. Int. Conf. Unmanned Aircr. Syst. (ICUAS), (2016), pp. 1254–1260. [Google Scholar]
  9. R. Stolkin, D. Rees, M. Talha, and I. Florescu, “Bayesian fusion of thermal and visible spectra camera data for mean shift tracking with rapid background adaptation”, in Proc. IEEE SENSORS, (2012), pp. 1–4. [Google Scholar]
  10. A. Coluccia, et al., “Drone-vs-bird detection challenge at IEEE AVSS2019”, in Proc. 16th IEEE Int. Conf. Adv. Video Signal Based Surveillance (AVSS), (2019), pp. 1–7. [Google Scholar]
  11. M. Nijim and N. Mantrawadi, “Drone classification and identification system by phenome analysis using data mining techniques”, in Proc. IEEE Symp. Technol. Homeland Security (HST), (2016), pp. 1–5. [Google Scholar]
  12. M. Benyamin and G. H. Goldman, “Acoustic Detection and Tracking of a Class I UAS with a Small Tetrahedral Microphone Array”, Army Res. Lab., Adelphi, MD, USA, Sep. (2014). 47 [Google Scholar]
  13. J. Busset, et al., “Detection and tracking of drones using advanced acoustic cameras”, in Proc. Unmanned/Unattended Sens. Sens. Netw. XI Adv. Free-Space Opt. Commun. Techn. Appl., vol. 9647, Oct. (2015), Art. no. 96470F. Appl. [Google Scholar]
  14. P. Nguyen, M. Ravindranatha, A. Nguyen, R. Han, and T. Vu, “Investigating cost-effective RF-based detection of drones”, in Proc. 2nd Workshop Micro Aerial Veh. Netw. Syst. Civilian Use, (2016), https://doi.org/10.1145/2935620.2935632pp.17–22.[Online]. [Google Scholar]
  15. P. Kosolyudhthasarn, V. Visoottiviseth, D. Fall, and S. Kashihara, “Drone detection and identification by using packet length signature”, in Proc. 15th Int. Joint Conf. Comput. Sci. Softw. Eng. (JCSSE), (2018), pp. 1–6. [Google Scholar]
  16. I. Bisio, C. Garibotto, F. Lavagetto, A. Sciarrone, and S. Zappatore, “Blind detection: Advanced techniques for WiFi-based drone surveillance”, IEEE Trans. Veh. Technol., vol. 68, no. 1, pp. 938–946, Jan. (2019). [Google Scholar]
  17. M. F. Al-Sa’d, A. Al-Ali, A. Mohamed, T. Khattab, and A. Erbad, “RF-based drone detection and identification using deep learning approaches: An initiative towards a large open-source drone database”, Future Gener. Comput. Syst., vol. 100, Nov. (2019), pp. 86–97. [Google Scholar]
  18. S. Al-Emadi and F. Al-Senaid, “Drone detection approach based on radio-frequency using convolutional neural network”, in Proc. IEEE Int. Conf. Inform. IoT Enabling Technol. (ICIoT), (2020), pp. 29–34. [Google Scholar]
  19. M. M. Azari, H. Sallouha, A. Chiumento, S. Rajendran, E. Vinogradov, and S. Pollin, “Key technologies and system trade-offs for detection and localization of amateur drones”, IEEE Commun. Mag., vol. 56, no. 1, pp. 51–57, Jan. (2018). [Google Scholar]
  20. P. Stoica, S. Basak, C. Molder, and B. Scheers, “Review of counter-UAV solutions based on the detection of remote-control communication”, in Proc. 13th Int. Conf. Commun. (COMM), (2020), pp. 233–238. [Google Scholar]
  21. S. Basak and B. Scheers, “Passive radio system for real-time drone detection and DoA estimation”, in Proc. Int. Conf. Military Commun. Inf. Syst. (ICMCIS), May (2018), pp. 1–6. [Google Scholar]
  22. S. Basak, S. Rajendran, S. Pollin, and B. Scheers, “Drone classification from RF fingerprints using deep residual nets”, in Proc. Int. Conf. Commun. Syst. Netw. (COMSNETS), (2021), pp. 548–555. [Google Scholar]
  23. H. Sun, A. Nallanathan, C.-X. Wang, and Y. Chen, “Wideband spectrum sensing for cognitive radio networks: A survey”, IEEE Wireless Commun., vol. 20, no. 2, pp. 74–81, Apr. (2013). 48 [Google Scholar]
  24. I. Kakalou, D. Papadopoulou, T. Xifilidis, K. E. Psannis, K. Siakavara, and Y. Ishibashi, “A survey on spectrum sensing algorithms for cognitive radio networks”, in Proc. 7th Int. Conf. Modern Circuits Syst. Technol. (MOCAST), (2018), pp. 1–4. [Google Scholar]
  25. B. Scheers, D. Teguig, and V. Le Nir, “Wideband spectrum sensing technique based on goodness-of-fit testing”, in Proc. Int. Conf. Military Commun. Inf. Syst. (ICMCIS), (2015), pp. 1–6. [Google Scholar]

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