A Swarm Intelligence Approach to \(3\)D Distance-Based Indoor UWB Localization

  • Stefania Monica
  • Gianluigi Ferrari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9028)


In this paper, we focus on the application of Ultra Wide Band (UWB) technology to the problem of locating static nodes in three-dimensional indoor environments, assuming to know the positions of a few nodes, denoted as “beacons.” The localization algorithms which are considered throughout the paper are based on the Time Of Arrival (TOA) of signals traveling between pairs of nodes. In particular, we propose to apply the Particle Swarm Optimization (PSO) algorithm to solve the localization problem and we compare its performance with that of the Two-Stage Maximum-Likelihood (TSML) algorithm. Simulation results show that the former allows achieving accurate position estimates even in scenarios where, because of ill-conditioning problems associated with the network topology, TSML fails.


Wireless Sensor Networks Localization Time Of Arrival (TOA) Maximum-Likelihood Particle Swarm Optimization 


  1. 1.
    Monica, S., Ferrari, G.: Accurate indoor localization with UWB wireless sensor networks. In: Proceedings of the 23rd IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2014), Track on Capacity-Driven Processes and Services for the Cyber-Physical Society (CPS), Parma, Italy, pp. 287–289 (2014)Google Scholar
  2. 2.
    Foy, W.H.: Position-location solutions by Taylor-series estimation. IEEE Trans. Aerosp. Electron. Syst. 12(2), 187–194 (1976)CrossRefGoogle Scholar
  3. 3.
    Mensing, C., Plass, S.: Positioning algorithms for cellular networks using TDOA. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), Toulouse, France, pp. 513–516 (2006)Google Scholar
  4. 4.
    Monica, S., Ferrari, G.: An experimental model for UWB distance measurements and its application to localization problems. In: Proceedings of the IEEE International Conference on Ultra Wide Band (ICUWB 2014), Paris, France, pp. 297–302 (2014)Google Scholar
  5. 5.
    Schmidt, R.O.: A new approach to geometry of range difference location. IEEE Trans. Aerosp. Electron. Syst. 8(6), 821–835 (1972)CrossRefGoogle Scholar
  6. 6.
    Monica, S., Ferrari, G.: Optimized anchors placement: An analytical approach in UWB-based TDOA localization. In: Proceedings of the 9th International Wireless Communications & Mobile Computing Conference (IWCMC 2013), Cagliari, Italy, pp. 982–987 (2013)Google Scholar
  7. 7.
    Chan, Y., Ho, K.C.: A simple and efficient estimator for hyperbolic location. IEEE Trans. Signal Process. 42(8), 1905–1915 (1994)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Shen, G., Zetik, R., Thomä, R.S.: Performance comparison of TOA and TDOA based location estimation algorithms in LOS environment. In: Proceedings of the 5th Workshop on Positioning, Navigation and Communication (WPNC 2008), Hannover, Germany, pp. 71–78 (2008)Google Scholar
  9. 9.
    Monica, S., Ferrari, G.: Particle swarm optimization for auto-localization of nodes in wireless sensor networks. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds.) ICANNGA 2013. LNCS, vol. 7824, pp. 456–465. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  10. 10.
    Monica, S., Ferrari, G.: Impact of the number of beacons in PSO-based auto-localization in UWB networks. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 42–51. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  11. 11.
    Monica, S., Ferrari, G.: UWB-based localization in large indoor scenarios: optimized placement of anchor nodes. IEEE Trans. Aerosp. Electron. Syst. (2015, to appear)Google Scholar
  12. 12.
    Monica, S., Ferrari, G.: Swarm intelligent approaches to auto-localization of nodes in static UWB networks. Appl. Soft Comput. 25, 426–434 (2014)CrossRefGoogle Scholar
  13. 13.
    Okamoto, E., Horiba, M., Nakashima, K., Shinohara, T., Matsumura, K.: Particle swarm optimization-based low-complexity three-dimensional UWB localization scheme. In: Proceedings of the International Conference on Ubiquitous and Future Networks, pp. 120–124 (2014)Google Scholar
  14. 14.
    Zhang, J., Orlik, P.V., Sahinoglu, Z., Molisch, A.F., Kinney, P.: UWB systems for wireless sensor networks. Proc. IEEE 97(2), 313–331 (2009)CrossRefGoogle Scholar
  15. 15.
    Busanelli, S., Ferrari, G.: Improved ultra wideband-based tracking of twin-receiver automated guided vehicles. J. Integr. Comput. Aided Eng. 19(1), 3–22 (2012)Google Scholar
  16. 16.
    Molisch, A.F., Cassioli, D., Chong, C.C., Emami, S., Fort, A., Kannan, B., Karedal, J., Kunisch, J., Schantz, H.G., Siwiak, K., Win, M.Z.: A comprehensive standardized model for ultrawideband propagation channels. IEEE Trans. Antennas Propag. 54(11), 3151–3166 (2006)CrossRefGoogle Scholar
  17. 17.
    Dardari, D., Chong, C.C., Win, M.Z.: Threshold-based time-of-arrival estimators in UWB dense multipath channels. IEEE Trans. Commun. 56(8), 1366–1378 (2008)CrossRefGoogle Scholar
  18. 18.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN), Perth, Australia, pp. 1942–1948 (1995)Google Scholar
  19. 19.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. J. 1(1), 33–57 (2007)CrossRefGoogle Scholar
  20. 20.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC), Washington, DC, pp. 69–73 (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Wireless Ad-hoc and Sensor Networks Laboratory, Department of Information EngineeringUniversity of ParmaParmaItaly

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