Node Location Optimization for Localizing UAVs in Urban Scenarios

  1. Verde, Paula
  2. Ferrero-Guillén, Rubén
  3. Alija-Pérez, José-Manuel
  4. Martínez-Gutiérrez, Alberto
  5. Díez-González, Javier
  6. Perez, Hilde
  1. 1 Universidad de León
    info
    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

    Geographic location of the organization Universidad de León
Book:
Lecture Notes in Networks and Systems

ISSN: 2367-3370,  2367-3389

ISBN: 9783031180491,  9783031180507

Year of publication: 2022

Pages: 616-625

Type: Book chapter

DOI: 10.1007/978-3-031-18050-7_60 SCOPUS: 2-s2.0-85141717240 GOOGLE SCHOLAR lock_openOpen access editor

Sustainable development goals

info

SDG classification obtained using Aurora SDG artificial intelligence model.

Abstract

Unmanned-Aerial-Vehicles (UAV) widespread use has grown significantly in recent years. Their insertion in the civil sector allows their implementation in the agricultural and industrial sectors, as well as their use for surveillance or delivery applications. However, the efficient development of these applications depends on the drone’s ability to position itself autonomously. Although it is common to find drones with satellite positioning systems (GNSS), these systems are insufficient for autonomous navigation in urban or indoor environments. In these scenarios, the implementation of local positioning systems (LPS) is widely extended due to their adaptability capabilities. Through the optimal distribution of the sensors that constitute this system, they can adapt to almost any environment while also improving its performance. However, the complexity of this problem has been characterized as NP-Hard, which complicates its resolution. In this paper, a genetic algorithm is developed to optimize LPS in different environments. This algorithm, pioneer in the design of LPS for UAV localization, is tested on a generated urban environment. The results obtained denote the effectiveness of the methodology by obtaining location uncertainties significantly lower than GNSS.

View funding

Funding information

Acknowledgements. This work was supported by the Spanish Research Agency (AEI) under grant number PID2019-108277GB-C21/AEI/10.13039/501100011033.

Funders

  • Spanish Research Agency
    • PID2019-108277GB-C21/AEI/10.13039/501100011033

Bibliographic References

  • Gupta, S.G., Ghonge, D., Jawandhiya, P.M., et al.: Review of unmanned aircraft system (UAS). Int. J. Adv. Res. Comput. Eng. Technol. 2 (2013)
  • Semsch, E., Jakob, M., Pavlicek, D., Pechoucek, M.: Autonomous UAV surveillance in complex urban environments. In: 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 2, pp. 82–85. IEEE (2009)
  • Nikolic, J., Burri, M., Rehder, J., Leutenegger, S., Huerzeler, C., Siegwart, R.: A UAV system for inspection of industrial facilities. In: 2013 IEEE Aerospace Conference, pp. 1–8. IEEE (2013)
  • Chiang, W.-C., Li, Y., Shang, J., Urban, T.L.: Impact of drone delivery on sustainability and cost: realizing the UAV potential through vehicle routing optimization. Appl. Energy 242, 1164–1175 (2019)
  • Fernández-Caramés, T.M., Blanco-Novoa, O., Froiz-Míguez, I., Fraga-Lamas, P.: Towards an autonomous industry 4.0 warehouse: a UAV and blockchain-based system for inventory and traceability applications in big data-driven supply chain management. Sensors 19(10), 2394 (2019)
  • Kim, J., Kim, S., Ju, C., Son, H.I.: Unmanned aerial vehicles in agriculture: a review of perspective of platform, control, and applications. IEEE Access 7, 105100–105115 (2019)
  • Brunner, G., Szebedy, B., Tanner, S., Wattenhofer, R.: The urban last mile problem: autonomous drone delivery to your balcony. In: 2019 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1005–1012. IEEE (2019)
  • Pavlenko, T., Schütz, M., Vossiek, M., Walter, T., Montenegro, S.: Wireless local positioning system for controlled UAV landing in GNSS-denied environment. In: 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace), pp. 171–175. IEEE (2019)
  • Bijjahalli, S., Gardi, A., Sabatini, R.: GNSS performance modelling for positioning and navigation in urban environments. In: 2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace), pp. 521–526. IEEE (2018)
  • Arifin, M., Nazaruddin, Y., Tamba, T., Santosa, R., Widyotriatmo, A.: Experimental modeling of a quadrotor UAV using an indoor local positioning system. In: 2018 5th International Conference on Electric Vehicular Technology (ICEVT), pp. 25–30. IEEE (2018)
  • Amar, A., Weiss, A.J.: Localization of narrowband radio emitters based on doppler frequency shifts. IEEE Trans. Signal Process. 56(11), 5500–5508 (2008)
  • Wang, Y., Ma, X., Leus, G.: Robust time-based localization for asynchronous networks. IEEE Trans. Signal Process. 59(9), 4397–4410 (2011)
  • Zhao, W., Panerati, J., Schoellig, A.P.: Learning-based bias correction for time difference of arrival ultra-wideband localization of resource-constrained mobile robots. IEEE Robot. Automat. Lett. 6(2), 3639–3646 (2021)
  • Deng, Z., Tang, S., Deng, X., Yin, L., Liu, J.: A novel location source optimization algorithm for low anchor node density wireless sensor networks. Sensors 21(5), 1890 (2021)
  • Khalaf-Allah, M.: Novel solutions to the three-anchor TOA-based three-dimensional positioning problem. Sensors 21(21), 7325 (2021)
  • Álvarez, R., Díez-González, J., Alonso, E., Fernández-Robles, L., Castejón-Limas, M., Perez, H.: Accuracy analysis in sensor networks for asynchronous positioning methods. Sensors 19(13), 3024 (2019)
  • Hu, J., et al.: A brief review on the positioning technologies for unmanned aerial vehicles. In: 2017 IEEE International Conference on Unmanned Systems (ICUS), pp. 527–532. IEEE (2017)
  • Nguyen, N.-T., Liu, B.-H.: The mobile sensor deployment problem and the target coverage problem in mobile wireless sensor networks are np-hard. IEEE Syst. J. 13(2), 1312–1315 (2018)
  • Díez-González, J., et al.: Genetic algorithm approach to the 3d node localization in TDOA systems. Sensors 19(18), 3880 (2019)
  • Rajakumar, R., Amudhavel, J., Dhavachelvan, P., Vengattaraman, T.: GWO-LPWSN: grey wolf optimization algorithm for node localization problem in wireless sensor networks. J. Comput. Netw. Commun. 2017 (2017)
  • Maheshwari, P., Sharma, A.K., Verma, K.: Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Netw. 110, 102317 (2021)
  • Kannan, A.A., Mao, G., Vucetic, B.: Simulated annealing based wireless sensor network localization. J. Comput. 1(2), 15–22 (2006)
  • Annepu, V., Rajesh, A.: Implementation of self adaptive mutation factor and cross-over probability based differential evolution algorithm for node localization in wireless sensor networks. Evolution. Intell. 12(3), 469–478 (2019)
  • Bajovic, D., Sinopoli, B., Xavier, J.: Sensor selection for event detection in wireless sensor networks. IEEE Trans. Signal Process. 59(10), 4938–4953 (2011)
  • Vankayalapati, N., Kay, S., Ding, Q.: TDOA based direct positioning maximum likelihood estimator and the Cramer-Rao bound. IEEE Trans. Aerosp. Electron. Syst. 50(3), 1616–1635 (2014)
  • Díez-González, J., Verde, P., Ferrero-Guillén, R., Álvarez, R., Pérez, H.: Hybrid memetic algorithm for the node location problem in local positioning systems. Sensors 20(19), 5475 (2020)
  • Yang, B., Scheuing, J.: Cramer-Rao bound and optimum sensor array for source localization from time differences of arrival. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), vol. 4, pp. iv–961. IEEE (2005)
  • Kaune, R., Hörst, J., Koch, W.: Accuracy analysis for TDOA localization in sensor networks. In: 14th International Conference on Information Fusion, pp. 1–8. IEEE (2011)
  • Díez-González, J., et al.: Optimal node distribution in wireless sensor networks considering sensor selection. In: Sanjurjo González, H., et al. (eds.) SOCO 2021. AISC, vol. 1401, pp. 512–522. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-87869-6_49
  • Mirjalili, S.: Genetic algorithm. In: Evolutionary Algorithms and Neural Networks. SCI, vol. 780, pp. 43–55. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93025-1_4
  • Ferrero-Guillén, R., Díez-González, J., Álvarez, R., Pérez, H.: Analysis of the genetic algorithm operators for the node location problem in local positioning systems. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds.) HAIS 2020. LNCS (LNAI), vol. 12344, pp. 273–283. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61705-9_23
  • Wang, L., Groves, P.D., Ziebart, M.K.: GNSS shadow matching: improving urban positioning accuracy using a 3D city model with optimized visibility scoring scheme. NAVIGATION, J. Inst. Navigat. 60(3), 195–207 (2013)
  • Bouloukou, M., Masiero, A., Vettore, A., Gikas, V.: UAV UWB positioning close to building facades: a case study. Int. Archiv. Photogram. Remote Sens. Spatial Inf. Sci. 43, 97–102 (2021)