Skip to main content
SpringerLink
Log in

Improved MDS-based Localization with Non-line-of-sight RF Links

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

The performance of indoor localization techniques adopted in robot localization systems that use radio positioning is usually degraded in non-line-of-sight (NLOS) environments. In this paper, we propose a technique for estimating NLOS biases and measurement noise in distances under multidimensional scaling (MDS) based positioning with fixed nodes. An ideal matrix of pairwise distance measurements exhibits a symmetry that allows to compute mobile node positions from those of fixed ones, and then recompute exactly the fixed node positions from the earlier computed mobile node positions. In a NLOS environment, this symmetry is lost; fixed node positions can not be reproduced exactly. This work exploits the error in the recomputation of fixed node positions for the correction of NLOS biases and noise in the pairwise distances. A constrained-optimization problem is formulated to estimate biases for each measured distance and final mobile node positions under the MDS scheme. A supplementary approach is presented for special cases where the number of mobile nodes is less than 3. Experimental results show that position errors can be reduced by up to 28% for a set up of 4 fixed and 3 mobile nodes. Simulations are used to further validate the results for larger deployments of nodes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. nanoLoc development kit user guide. Nanotron Technologies GmbH, Berlin, Germany, Technical Report NA-06-0230-0402-1.03 (2007)

  2. Ahmed, A.A., Shi, H., Shang, Y.: Sharp: a new approach to relative localization in wireless sensor networks. In: 25Th IEEE International Conference on Distributed Computing Systems Workshops, pp. 892–898. https://doi.org/10.1109/ICDCSW.2005.125 (2005)

  3. Alavi, B., Pahlavan, K.: Modeling of the distance error for indoor geolocation. In: 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003., Vol. 1, pp. 668–672 (2003)

  4. Bai, Y., Jia, W., Zhang, H., Mao, Z., Sun, M.: Landmark-based indoor positioning for visually impaired individuals. In: 2014 12Th International Conference on Signal Processing (ICSP), pp. 668–671. https://doi.org/10.1109/ICOSP.2014.7015087 (2014)

  5. Borg, I., Groenen, P.: Modern multidimensional scaling: Theory and applications. Springer, Berlin (2005)

    MATH  Google Scholar 

  6. Borras, J., Hatrack, P., Mandayam, N.B.: Decision theoretic framework for NLOS identification. In: Vehicular Technology Conference, 1998. VTC 98. 48Th IEEE, vol. 2, pp. 1583–1587 (1998)

  7. Cong, L., Zhuang, W.: Nonline-of-sight error mitigation in mobile location. IEEE Trans. Wirel. Commun. 4(2), 560–573 (2005)

    Article  Google Scholar 

  8. Di Franco, C., Bini, E., Marinoni, M., Buttazzo, G.C.: Multidimensional scaling localization with anchors. In: 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 49–54 (2017)

  9. Di Franco, C., Melani, A., Marinoni, M.: Solving ambiguities in MDS relative localization. In: 2015 International Conference on Advanced Robotics (ICAR), pp. 230–236 (2015)

  10. Galov, A., Moschevikin, A., Voronov, R.: Combination of Rss localization and Tof ranging for increasing positioning accuracy indoors. In: 2011 11Th International Conference on ITS Telecommunications, pp. 299–304. https://doi.org/10.1109/ITST.2011.6060072 (2011)

  11. Güvenç, İ., Chong, C.C., Watanabe, F.: NLOS identification and mitigation for UWB localization systems. In: 2007 IEEE Wireless Communications and Networking Conference, pp. 1571–1576 (2007)

  12. Güvenç, İ., Chong, C.C., Watanabe, F., Inamura, H.: NLOS identification and weighted least-squares localization for UWB systems using multipath channel statistics. EURASIP Journal on Advances in Signal Processing 2008(1), 271984 (2007)

    Article  Google Scholar 

  13. Heidari, M., Akgul, F.O., Alsindi, N.A., Pahlavan, K.: Neural network assisted identification of the absence of direct path in indoor localization. In: IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference, pp. 387–392 (2007)

  14. Heidari, M., Akgul, F.O., Pahlavan, K.: Identification of the absence of direct path in indoor localization systems. In: 2007 IEEE 18Th International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1–6 (2007)

  15. Holm, S.: Ultrasound positioning based on Time-Of-Flight and signal strength. In: 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–6. https://doi.org/10.1109/IPIN.2012.6418728 (2012)

  16. Koledoye, M.A., Facchinetti, T., Almeida, L.: MDS-based localization with known anchor locations and missing tag-to-tag distances. In: 2017 22Nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Pp. 1–4 (2017)

  17. Koledoye, M.A., Facchinetti, T., Almeida, L.: Mitigating effects of NLOS propagation in MDS-based localization with anchors. In: IEEE International Conference on Autonomous Robot Systems and Competitions ICARSC (2018)

  18. de Leeuw, J.: Convergence of the majorization method for multidimensional scaling. J. Classif. 5(2), 163–180 (1988)

    Article  MathSciNet  Google Scholar 

  19. de Leeuw, J., Meulman, J.: A special jackknife for multidimensional scaling. J. Classif. 3(1), 97–112 (1986)

    Article  Google Scholar 

  20. Leeuw, J.D., Barra, I.J.R., Brodeau, F., Romier, G. (eds.): B.V.C.: Applications of convex analysis to multidimensional scaling. North Holland Publishing Company, Amsterdam (1977)

    Google Scholar 

  21. Li, X., Shi, H., Shang, Y.: Sensor network localisation based on sorted rssi quantisation. Int. J. Ad Hoc Ubiquitous Comput. 1(4), 222–229 (2006)

    Article  Google Scholar 

  22. Patwari, N., Hero, A.O., Perkins, M., Correal, N.S., O’Dea, R.J.: Relative location estimation in wireless sensor networks. IEEE Trans. Signal Process. 51(8), 2137–2148 (2003). https://doi.org/10.1109/TSP.2003.814469

    Article  Google Scholar 

  23. Powell, M.J.D.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 7(2), 155–162 (1964)

    Article  MathSciNet  Google Scholar 

  24. Röhrig, C., Müller, M.: Indoor location tracking in non-line-of-sight environments using a Ieee 802.15.4A wireless network. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 552–557 (2009)

  25. Shang, Y., Ruml, W., Zhang, Y., Fromherz, M.P.J.: Localization from mere connectivity. In: Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking &Amp; Computing, MobiHoc ’03, pp. 201–212. ACM, New York (2003)

  26. Shi, W., Wong, V.W.S.: MDS-based localization algorithm for RFID systems. In: 2011 IEEE International Conference on Communications (ICC), pp. 1–6 (2011)

  27. Ussmueller, T., Brenk, D., Essel, J., Heidrich, J., Fischer, G., Weigel, R.: Roundtrip-time-of-flight based localization of passive multi-standard Rfid-Tags. In: 2012 IEEE International Conference on Wireless Information Technology and Systems (ICWITS), pp. 1–4. https://doi.org/10.1109/ICWITS.2012.6417725 (2012)

  28. Venkatesh, S., Buehrer, R.M.: Non-line-of-sight identification in ultra-wideband systems based on received signal statistics. IET Microwaves Antennas Propag. 1(6), 1120–1130 (2007)

    Article  Google Scholar 

  29. Wang, Q., Luo, H., Zhao, F., Shao, W.: An indoor self-localization algorithm using the calibration of the online magnetic fingerprints and indoor landmarks. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8. https://doi.org/10.1109/IPIN.2016.7743595 (2016)

  30. Wei, W., Jin-Yu, X., Zhong-Liang, Z.: A new NLOS error mitigation algorithm in location estimation. IEEE Trans. Veh. Technol. 54(6), 2048–2053 (2005). https://doi.org/10.1109/TVT.2005.858177

    Article  Google Scholar 

  31. Yu, K., Guo, Y.J.: Improved positioning algorithms for nonline-of-sight environments. IEEE Trans. Veh. Technol. 57(4), 2342–2353 (2008)

    Article  Google Scholar 

  32. Yu, K., Guo, Y.J.: Non-Line-Of-Sight Detection Based on Toa and Signal Strength. In: 2008 IEEE 19Th International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1–5 (2008)

  33. Yu, K., Guo, Y.J.: Statistical NLOS identification based on AOA, TOA, and signal strength. IEEE Trans. Veh. Technol. 58(1), 274–286 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the collaboration of Prof. Pedro Lima from IST in Lisbon, in providing extra equipment for the experiments.

Author information

Authors and Affiliations

  1. Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata, 6, 27100, Pavia, Italy

    Moses A. Koledoye & Tullio Facchinetti

  2. Centro de Investigação em Sistemas de Tempo-Real, Instituto de Telecomunicações, Universidade do Porto, Porto, Portugal

    Luis Almeida

Authors
  1. Moses A. Koledoye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tullio Facchinetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luis Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Moses A. Koledoye.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koledoye, M.A., Facchinetti, T. & Almeida, L. Improved MDS-based Localization with Non-line-of-sight RF Links. J Intell Robot Syst 98, 227–237 (2020). https://doi.org/10.1007/s10846-019-01021-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-019-01021-1

Keywords

Search

Navigation