A Novel Semidefinite Programming-based UAV 3D Localization Algorithm with Gray Wolf Optimization

  1. Li, Zhijia 1
  2. Xia, Xuewen 1
  3. Yan, Yonghang 12
  4. González Aguilera, Diego 3
  1. 1 School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
  2. 2 Henan Province Engineering Research Center of Spatial Information Processing, Henan University, Kaifeng 475004, China
  3. 3 Universidad de Salamanca
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    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

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Revista:
Drones

ISSN: 2504-446X

Año de publicación: 2023

Volumen: 7

Número: 2

Páginas: 113

Tipo: Artículo

DOI: 10.3390/DRONES7020113 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Drones

Resumen

The unmanned aerial vehicle (UAV) network has gained vigorous evolution in recent decades by virtue of its advanced nature, and UAV-based localization techniques have been extensively applied in a variety of fields. In most applications, the data captured by a UAV are only useful when associated with its geographic position. Efficient and low-cost positioning is of great significance for the development of UAV-aided technology. In this paper, we investigate an effective three-dimensional (3D) localization approach for multiple UAVs and propose a flipping ambiguity avoidance optimization algorithm. Specifically, beacon UAVs take charge of gaining global coordinates and collecting distance measurements from GPS-denied UAVs. We adopt a semidefinite programming (SDP)-based approach to estimate the global position of the target UAVs. Furthermore, when high noise interference causes missing distance pairs and measurement errors, an improved gray wolf optimization (I-GWO) algorithm is utilized to improve the positioning accuracy. Simulation results show that the proposed approach is superior to a number of alternative approaches.

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Información de financiación

Financiadores

  • science and technology research project of the Henan province
    • 222102240014

Referencias bibliográficas

  • Ashish, (2021), Comput. Sci. Rev., 39, pp. 100359, 10.1016/j.cosrev.2020.100359
  • Tang, (2020), Proc. IEEE, 108, pp. 292, 10.1109/JPROC.2019.2954595
  • Orfanus, (2016), IEEE Commun. Lett., 20, pp. 804, 10.1109/LCOMM.2016.2524405
  • Mozaffari, (2016), Wirel. Commun., 15, pp. 3949
  • Guo, (2020), Sci. China Inf. Sci., 63, pp. 119201, 10.1007/s11432-018-9603-y
  • Zheng, (2018), Signal Process., 143, pp. 134, 10.1016/j.sigpro.2017.08.025
  • Li, W., Jelfs, B., Kealy, A., Wang, X., and Moran, B. (2021). Cooperative Localization Using Distance Measurements for Mobile Nodes. Sensors, 21.
  • Xu, (2020), IEEE Sens. J., 20, pp. 9589
  • Kang, (2018), IEEE Trans. Ind. Inform., 15, pp. 173, 10.1109/TII.2018.2800047
  • Montiel, (2015), IEEE Trans. Robot., 31, pp. 1147, 10.1109/TRO.2015.2463671
  • Russell, (2019), IEEE Trans. Aerosp. Electron. Syst., 56, pp. 1966, 10.1109/TAES.2019.2942704
  • Arafat, (2019), IEEE Internet Things J., 6, pp. 8958, 10.1109/JIOT.2019.2925567
  • Yeasir, (2021), IEEE Access, 9, pp. 18649, 10.1109/ACCESS.2021.3053605
  • Biswas, (2006), IEEE Trans. Autom. Sci. Eng., 3, pp. 360, 10.1109/TASE.2006.877401
  • Guo, (2019), IEEE Trans. Cybern., 50, pp. 2590, 10.1109/TCYB.2019.2905570
  • Baldi, S., Roy, S., and Yang, K. (2020, January 14–18). Towards adaptive autopilots for fixed-wing unmanned aerial vehicles. Proceedings of the 2020 59th IEEE Conference on Decision and Control (CDC), Jeju, Republic of Korea.
  • Wang, (2022), IEEE/ASME Trans. Mechatron., 27, pp. 4597, 10.1109/TMECH.2022.3160480
  • Shang, Y., and Ruml, W. (2004, January 7–11). Improved MDS-based localization. Proceedings of the IEEE INFOCOM 2004, Hong Kong, China.
  • Wan, C., You, S., Jing, G., and Dai, R. (2019, January 10–12). A distributed algorithm for sensor network localization with limited measurements of relative distance. Proceedings of the 2019 American Control Conference (ACC), Philadelphia, PA, USA.
  • Zhang, (2012), Wirel. Pers. Commun., 63, pp. 261, 10.1007/s11277-011-0337-0
  • Parulpreet, (2018), AEU-Int. J. Electron. Commun., 91, pp. 55, 10.1016/j.aeue.2018.04.024
  • Liu, (2020), IEEE Trans. Veh. Technol., 69, pp. 11756, 10.1109/TVT.2020.3017162
  • Wang, (2018), KSII Trans. Internet Inf. Syst. (TIIS), 12, pp. 61
  • Nguyen, (2015), IEEE Trans. Veh. Technol., 64, pp. 1318, 10.1109/TVT.2015.2398874
  • Wang, (2013), IEEE Trans. Veh. Technol., 62, pp. 853, 10.1109/TVT.2012.2225074
  • Shang, (2004), IEEE Trans. Parallel Distrib. Syst., 15, pp. 961, 10.1109/TPDS.2004.67
  • Zou, (2018), IEEE Access, 6, pp. 2746, 10.1109/ACCESS.2017.2785182
  • Carlone, (2012), IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum., 42, pp. 1065, 10.1109/TSMCA.2012.2185045
  • Pratik, (2006), ACM Trans. Sens. Netw. (TOSN), 2, pp. 188, 10.1145/1149283.1149286
  • Ye, (2007), Math. Program., 109, pp. 367, 10.1007/s10107-006-0040-1
  • Anstreicher, (2009), J. Glob. Optim., 43, pp. 471, 10.1007/s10898-008-9372-0
  • Zheng, (2019), J. Frankl. Inst., 356, pp. 11817, 10.1016/j.jfranklin.2019.10.029
  • Raguraman, (2018), Appl. Soft Comput., 68, pp. 219, 10.1016/j.asoc.2018.03.039