PREDICTION OF UAV POSITIONS USING PARTICLE SWARM OPTIMISATION-BASED KALMAN FILTER

Jian Zhou, Yu Su, Yuhe Qiu, Xiaoyou He, and Zhihong Rao

References

  1. [1] L. Gupta, R. Jain, and G. Vaszkun, Survey of importantissues in UAV communication networks, IEEE CommunicationsSurveys and Tutorials, 18(2), 2016, 1123–1152.
  2. [2] J. Zhao, Research on state information prediction of UAVunder smart city, Ph.D. dissertation, Northern PolytechnicUniversity, 2020.
  3. [3] Z.M. Qi, Q.I. Huang, and H.L. Zhang, Intelligent unmannedcluster mission planning system architecture design, MilitaryOperations Research and Systems Engineering, 33(3), 2019,26–30.
  4. [4] H.B. Duan, L. Xin. Y. Xu, G. Zhao, Eagle-vision-inspiredvisual measurement algorithm for UAV’s autonomous landing,International Journal of Robotics and Automation, 35(2), 2020,94–100.
  5. [5] R. Dong, C. Liu, X. Wang, and X. Han 3D path planning ofUAVs for transmission lines inspection, International Journalof Robotics and Automation, 35, 2020, 146–158.
  6. [6] Y. Yan, Z. Lv, P. Huang, and J. Yuan, Rapid selecting UAVsfor combat based on three-way multiple attribute decision,International Journal of Robotics and Automation, 36, 2021,296–305.
  7. [7] V. Doan, T. Thang, V. Than Dung, and S. Hirotaka, A cyborginsect reveals a function of a muscle in free flight, Cyborg andBionic Systems 2022, 2022, 9780504.
  8. [8] L. Yu, J. Zhao, Z. Ma, W. Wang, S. Yan, Y. Jin, and Y.Fang, Experimental verification on steering flight of honeybeeby electrical stimulation, Cyborg and Bionic Systems 2022,2022, 9895837.
  9. [9] Z. Chen, Q. Liang, Z. Wei, X. Chen, Q. Shi, Z. Yu, and T.Sun, An overview of in vitro biological neural networks forrobot intelligence, Cyborg and Bionic Systems, 4, 2023, 0001.
  10. [10] C. Lidynia, R. Philipsen, and M. Ziefle, Droning on aboutdrones—Acceptance of and perceived barriers to drones incivil usage contexts, Proc. of the AHFE 2016 InternationalConference on Human Factors in Robots and UnmannedSystems, , Florida, USA, 2017,317–329.
  11. [11] M. Ayamga, S. Akaba, and A.A. Nyaaba, Multifacetedapplicability of drones: A review, Technological Forecastingand Social Change, 167, 2021, 120677.
  12. [12] X. He, Y. Su, and Y. Qiu, An improved unscented Kalmanfilter for maneuvering target tracking, Journal of Physics:Conference Series, 2216(1), 2022, 012010.
  13. [13] A. Hafeez, M.A. Husain, S.P. Singh, and A. Chauhan,Implementation of drone technology for farm monitoringand pesticide spraying: A review. Information Processing inAgriculture, 10(2), 2022, 192–203.
  14. [14] M. Sibanda, O. Mutanga, V.G.P. Chimonyo, A.D. Clulow, C.Shoko, D. Mazvimavi, T. Dube, T. Mabhaudhi, Application ofdrone technologies in surface water resources monitoring andassessment: A systematic review of progress, challenges, andopportunities in the global south, Drones, 5(3), 2021, 84.
  15. [15] S. Lee and Y. Choi, Reviews of unmanned aerial vehicle (drone)technology trends and its applications in the mining industry,Geosystem Engineering, 19(4), 2016, 197–204.
  16. [16] M. Hammer, M. Hebel, M. Laurenzis, and M. Arens, Lidar-Based Detection and Tracking of Small UAVs, Society ofPhotographic Instrumentation Engineers, Digital Library,Berlin, Germany, 2018, 107990S-1–107990S-9.
  17. [17] Z. Wu, J. Li, J. Zuo, and S. Li, Path planning of UAVs basedon collision probability and Kalman filter, IEEE Access, 62018, 34237–34245.
  18. [18] Y. Lin and S. Saripalli, Path planning using 3D Dubins curvefor unmanned aerial vehicles, International Conference onUnmanned Aircraft Systems (ICUAS), Florida, USA, 2014,296–304..
  19. [19] S. Becker, R. Hug, W. H¨ubner, M. Arens, and B.T. Morris,Generating synthetic training data for deep learning-basedUAV trajectory prediction, arXiv:2107.00422, 2021.
  20. [20] B. Hu, H. Yang, L. Wang, and S. Chen, A trajectory predictionbased intelligent handover control method in UAV cellularnetworks, China Communications, 16(1), 2019, 1–14.
  21. [21] P. Shu, C. Chen, B. Chen, K. Su, S. Chen, H. Liu, F. Huang, Tra-jectory prediction of UAV based on LSTM, Proc. 2nd Interna-tional Conference on Big Data & Artificial Intelligence & Soft-ware Engineering (ICBASE), Zhuhai, China, 2021, 448–451.
  22. [22] R. Havangi, An adaptive particle filter based on PSO and fuzzyinference system for nonlinear state systems, Automatika,59(1), 2018, 94–103.
  23. [23] Q. Bai, Analysis of particle swarm optimization algorithm,Computer and information science, 3(1), 2010, 180.
  24. [24] N. Hassan and A. Saleem, Real time obstacle motion predictionusing neural network based extended Kalman filter for robotpath planning, Kuwait Journal of Science, 50, 2021, 1–20.
  25. [25] F. Deng, H.-L. Yang, and L.-J. Wang, Adaptive unscentedKalman filter based estimation and filtering for dynamicpositioning with model uncertainties, International Journal ofControl, Automation and Systems, 17, 2019, 667–678.
  26. [26] W. Wenhui, S. Gao, Y. Zhong, C. Gu, and A. Subic, Randomweighting estimation for systematic error of observationmodel in dynamic vehicle navigation, International Journal ofControl, Automation and Systems, 14,2016, 514–523.

Important Links:

Go Back