International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

Advancements in Unmanned Aerial Vehicle Classification, Tracking, and Detection Algorithms

  • Received : 2023.06.19
  • Accepted : 2023.06.29
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper provides a comprehensive overview of UAV classification, tracking, and detection, offering researchers a clear understanding of these fundamental concepts. It elucidates how classification categorizes UAVs based on attributes, how tracking monitors real-time positions, and how detection identifies UAV presence. The interconnectedness of these aspects is highlighted, with detection enhancing tracking and classification aiding in anomaly identification. Moreover, the paper emphasizes the relevance of simulations in the context of drones and UAVs, underscoring their pivotal role in training, testing, and research. By succinctly presenting these core concepts and their practical implications, the paper equips researchers with a solid foundation to comprehend and explore the complexities of UAV operations and the role of simulations in advancing this dynamic field.

Keywords

Acknowledgement

This work was supported by the 2023 research fund of Kyungdong University.

References

  1. M. Hassanalian, D. Rice and A. Abdelkefi, "Evolution of space drones for planetary exploration: A review", Prog. Aerosp. Sci., vol. 97, no. January, pp. 3560, 2018.
  2. Goldman Sachs Research,"Drones reporting for work",2017, [online] Available: http://www.goldmansachs.com/our-thinking/technology-driving-innovation/drones/.Accessed on Sept 1, 2020
  3. Federal Aviation Administration,"FAA Aerospace Forecast, FY 2016-2036",2016, [online] Available: https://www.faa.gov/data_research/aviation/aerospace_forecasts/media/FY2016-36_FAA_Aerospace_Forecast.pdf
  4. X. Wang, A. Chowdhery and M. Chiang, "Skyeyes: Adaptive video streaming from UAVs", Proc. ACM HotWireless Workshop, pp. 2-6.
  5. H. Wang, H. Zhao, J. Zhang, D. Ma, J. Li and J. Wei, "Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective," in IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1027-1070, Secondquarter 2020, doi: 10.1109/COMST.2019.2962207.
  6. J-P. Yaacoub, H. Noura, O. Salman and A. Chehab,"Security analysis of drones systems: Attacks, limitations, and recommendations," in Internet of Things, vol. 11, no. 100218, pp. 1-39, 2020 https://doi.org/10.1016/j.iot.2020.100218
  7. P. Boccadoro, D. Striccoli and L. A. Grieco,"Internet of Drones: a Survey on Communications, Technologies, Protocols, Architectures and Services,"2020. [Online]. Available: arXiv:2007.12611
  8. N. Hossein Motlagh, T. Taleb and O. Arouk, "Low-Altitude Unmanned Aerial Vehicles-Based Internet of Things Services: Comprehensive Survey and Future Perspectives," in IEEE Internet of Things Journal, vol. 3, no. 6, pp. 899-922, Dec. 2016, doi: 10.1109/JIOT.2016.2612119.
  9. R. Altawy and A. M. Youssef, "Security privacy and safety aspects of civilian drones: A survey", ACM Trans. Cyber-Physical Systems, vol. 1, no. 2, pp. 7, 2016.
  10. B. Nassi, A. Shabtai, R. Masuoka and Y. Elovici, "SoK-Security and privacy in the age of drones: Threats challenges solution mechanisms and scientific gaps", arXiv:1903.05155, 2019, [online] Available: https://arxiv.org/abs/1903.05155.
  11. M. Ritchie, F. Fioranelli, and H. Borrion, "Micro UAV crime prevention: can we help Princess Leia in Preventing Crime Problems around the Globe through Research Innovations in the 21st Century." B. L. Savona and E., Eds., TBA.
  12. T. Eshel, "Mobile radar optimized to detect uavs, precision guided weapons," Defense Update, 2013.
  13. Zhang, D.X. Research on electromagnetic interference mechanism of main remote control data link of UAV. J. Microwave 2016, 32, 90-96.
  14. Jiang, Z.J.; Cheng, X.G.; Peng, Y.Q. Research on UAV identification algorithm based on deeplearning. App. Elec. Tech. 2017, 43, 84-87.
  15. Zhao, J.C.; Fu, X.R.; Yang, Z.K.; Xu, F.T. Radar-Assisted UAV Detection and Identification Based on 5G in the Internet of Things. Wirel. Commun. Mob. Comput. 1-12, 2019.
  16. Mohsan, S.A.H.; Khan, M.A.; Noor, F.; Ullah, I.; Alsharif, M.H. Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review. Drones 2022, 6, 147. https://doi.org/10.3390/drones6060147
  17. R. Fu, X. Ren, Y. Li, Y. Wu, H. Sun and M. A. Al-Absi, "Machine Learning-Based UAV Assisted Agricultural Information Security Architecture and Intrusion Detection," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2023.3236322.
  18. Fu, R.; Al-Absi, M.A.; Lee, Y.-S.; Al-Absi, A.A.; Lee, H.J. Modified Uncertainty Error Aware Estimation Model for Tracking the Path of Unmanned Aerial Vehicles. Appl. Sci. 2022, 12, 11313. https://doi.org/10.3390/app122211313
  19. Al-Absi, M.A.; Fu, R.; Kim, K.-H.; Lee, Y.-S.; Al-Absi, A.A.; Lee, H.-J. Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware. Electronics 2021, 10, 3067. https://doi.org/10.3390/electronics10243067
  20. R. Fu, M. A. Al-Absi, K. -H. Kim, Y. -S. Lee, A. A. Al-Absi and H. -J. Lee, "Deep Learning-Based Drone Classification Using Radar Cross Section Signatures at mmWave Frequencies," in IEEE Access, vol. 9, pp. 161431-161444, 2021, doi: 10.1109/ACCESS.2021.3115805.
  21. E. Capello, G. Guglieri, F. B. Quagliotti, Uavs and simulation: an experience on mavs 81 (2009) 38-50.
  22. I. Bekmezci, O. K. Sahingoz, S_. Temel, Flying ad-hoc networks (fanets): A survey, Ad Hoc Networks 11 (3) (2013)
  23. B. Kate, J. Waterman, K. Dantu, M. Welsh, Simbeeotic: A simulator and testbed for micro-aerial vehicle swarm experiments. (2012) 49{60doi:10.1109/IPSN.2012.6920950.
  24. J. Berndt, Jsbsim: An open source ight dynamics model in c++, in: AIAA Modeling and Simulation Technologies Conference and Exhibit, 2004, p. 4923.
  25. J. Van West, K. Lane-Cummings, Microsoft Flight Simulator X for Pilots: Real World Training, John Wiley & Sons, 2007.
  26. B. Stack, FlightSim.Com - Review: MQ-1 UAV Predator by Abacus, https://tinyurl.com/ybytg3bq, (Accessed: August 16, 2018) (April 2012).
  27. Introduction FlightGear Flight Simulator, http://home.flightgear.org/about/, (Accessed: June 25, 2022).
  28. A. R. Perry, The ightgear ight simulator, in: Proceedings of the USENIX Annual Technical Conference, 2004.
  29. E. A. Marconato, M. Rodrigues, R. d. M. Pires, D. F. Pigatto, C. Q. Luiz Filho, A. R. Pinto, K. R. Branco, AVENS-a novel ying ad hoc network simulator with automatic code generation for unmanned aircraft system, in: Proceedings of the 50th Hawaii International Conference on System Sciences, 2017.
  30. A. Y. Javaid, W. Sun, M. Alam, UAVSim: A simulation testbed for unmanned aerial vehicle network cyber security analysis, in: Globecom Workshops (GC Wkshps), 2013 IEEE, IEEE, 2013, pp. 1432-1436.
  31. A. Y. Javaid, Cyber security threat analysis and attack simulation for unmanned aerial vehicle network.
  32. T. Du, A. Schulz, B. Zhu, B. Bickel, W. Matusik, Computational multicopter design, ACM Transactions on Graphics. (TOG) 35 (6) (2016) 227.
  33. S. Shah, D. Dey, C. Lovett, A. Kapoor, Airsim: High-_delity visual and physical simulation for autonomous vehicles, in: Field and Service Robotics, 2017. arXiv:1705.05065.
  34. F. Furrer, M. Burri, M. Achtelik, R. Siegwart, Robot operating system (ros), Studies Comp.Intelligence Volume Number: 625 The Complete Reference (Volume 1) (978-3-319-26052-5) (2016) Chapter 23, iSBN:978-3-319-26052-5.
  35. W. G. La, S. Park, H. Kim, D-muns: Distributed multiple uavs network simulator (2017) 15{17doi:10.1109/ICUFN. 2017.7993738.
  36. J. M. Kok, J. S. Chahl, A low-cost simulation platform for apping wing mavs, Proc.SPIE 9429 (2015) 9429-9429-7. doi:10.1117/12.2084142.
  37. Unmanned Aircraft Systems - Vampire, https://aegistg.com/unmanned-aircraft-systems-vampire/, (Accessed: June 28, 2022).
  38. Real Drone Simulator, http://www.realdronesimulator.com/, (Accessed: June 28, 2022).
  39. Zephyr-Sim, https://zephyr-sim.com/, (Accessed: June 28, 2022).
  40. A. Pruitt, Hone your drone piloting skills with the Zephyr simulator - TechRepublic, https://tinyurl.com/y8g4byaf, (Accessed: June 28, 2022).