Skip to main content
SpringerLink
Log in

An Improved Quadratic Programming LLOP Algorithm for Wireless Localization

  • Conference paper
  • First Online:
Machine Learning and Intelligent Communications (MLICOM 2018)

  • 1573 Accesses

Abstract

With the rapid increasing of smart devices, wireless positioning technology has become a hot research area. Accordingly, this paper puts forward an optimization-based localization in the wireless network, in which both the quadratic programming (QP) and the principle of linear line of position (LLOP) are taken into account. Moreover, a two-step improvement is proposed to enhance the constrained optimization model, and the simulations demonstrate its effectiveness. Among the tested localization methods, the proposed algorithm performs the best in the non-line-of-sight (NLOS) propagating environment, and its estimating stability over original LLOP algorithm is also obviously observed.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Reed, J., Rappaport, T.: An overview of the challenges and progress in meeting the E-911 requirement for location service. IEEE Commun. Mag. 36(4), 30–37 (1998)

    Article  Google Scholar 

  2. Konings, D., Faulkner, N., Alam, F., et al.: Do RSSI values reliably map to RSS in a localization system. In: Proceedings of the IEEE Recent Trends in Telecommunications Research, pp. 1–5 (2017)

    Google Scholar 

  3. Yang, C.Y., Chen, B.S., Liao, F.K.: Mobile location estimation using fuzzy-based IMM and data fusion. IEEE Trans. Mobile Comput. 9(10), 1424–1436 (2010)

    Article  Google Scholar 

  4. Liu, D., Wang, Y., He, P., et al.: TOA localization for multipath and NLOS environment with virtual stations. EURASIP J. Wirel. Commun. Netw. (2017). https://doi.org/10.1186/s13638-017-0896-1

  5. Bnilam, N., Ergeerts, G., Subotic, D., et al.: Adaptive probabilistic model using angle of arrival estimation for IoT indoor localization. In: Proceedings of the IEEE International Conference on Indoor Positioning and Indoor Navigation, pp. 1–7 (2017)

    Google Scholar 

  6. Tomic, S., Beko, M., Rui, D., et al.: A closed-form solution for RSS/AoA target localization by spherical coordinates conversion. IEEE Wirel. Commun. Lett. 5(6), 680–683 (2016)

    Article  Google Scholar 

  7. Huang, B., Xie, L., Yang, Z.: TDOA-based source localization with distance-dependent noises. IEEE Trans. Wirel. Commun. 14(1), 468–480 (2015)

    Article  Google Scholar 

  8. Gholami, M.R., Gezici, S., Strom, E.G.: TDOA based positioning in the presence of unknown clock skew. IEEE Trans. Commun. 61(6), 2522–2534 (2013)

    Article  Google Scholar 

  9. Khalajmehrabadi, A., Gatsis, N., Pack, D., et al.: A joint indoor WLAN localization and outlier detection scheme using LASSO and elastic-net optimization techniques. IEEE Trans. Mobile Comput. 16(8), 2079–2092 (2017)

    Article  Google Scholar 

  10. Narzullaev, A., Park, Y., Yoo, K., et al.: A fast and accurate calibration algorithm for real-time locating systems based on the received signal strength indication. AEU-Int. J. Electron. Commun. 65(4), 305–311 (2011)

    Article  Google Scholar 

  11. Demeechai, T., Kukieattikool, P., Ngo, T., et al.: Localization based on standard wireless LAN infrastructure using MIMO-OFDM channel state information. EURASIP J. Wirel. Commun. Netw. 146, 1–16 (2016)

    Google Scholar 

  12. Pecoraro, G., Domenico, S.D., Cianca, E., et al.: LTE signal fingerprinting localization based on CSI. In: Proceedings of the IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, pp. 1–8 (2017)

    Google Scholar 

  13. Lo, Y.C., Chiu, C.C., Huang, C.H.: Mitigating NLOS error for UWB positioning system. In: Proceedings of the IET International Conference on Wireless, Mobile and Multimedia Networks, pp. 1–3 (2006)

    Google Scholar 

  14. Silventoinen, M.I., Rantalainen, T.: Mobile station emergency locating in GSM. In: Proceedings of the IEEE International Conference on Personal Wireless Communications, pp. 232–238 (1996)

    Google Scholar 

  15. Wylie, M.P., Holtzman, J.: The non-line of sight problem in mobile location estimation. In: Proceedings of the IEEE International Conference on Universal Personal Communications, pp. 827–831 (1996)

    Google Scholar 

  16. Muqaibel, A.H., Landolsi, M.A., Mahmood, M.N.: Practical evaluation of NLOS/LOS parametric classification in UWB channels. In: Proceedings of the International Conference on Communications, Signal Processing, and Their Applications, pp. 1–6 (2013)

    Google Scholar 

  17. Almazrouei, E., Sindi, N.A., Al-Araji, S.R., et al.: Measurement and analysis of NLOS identification metrics for WLAN systems. In: Proceedings of the IEEE International Symposium on Personal, Indoor, and Mobile Radio Communication, pp. 280–284 (2015)

    Google Scholar 

  18. Chan, Y.W.E., Soong, B.H.: Discrete weighted centroid localization (dWCL): performance analysis and optimization. IEEE Access 4, 6283–6294 (2016)

    Article  Google Scholar 

  19. Yang, K., An, J., Bu, X., et al.: A TOA-based location algorithm for NLOS environments using quadratic programming. In: Proceedings of the IEEE Wireless Communications and Networking Conference, pp. 1–5 (2010)

    Google Scholar 

  20. Al-Qahtani, K.M., Al-Ahmari, A.S., Muqaibel, A.H., et al.: Improved residual weighting for NLOS mitigation in TDOA-based UWB positioning systems. In: Proceedings of the International Conference on Telecommunications, pp. 211–215 (2014)

    Google Scholar 

  21. Zheng, X., Hua, J., Zheng, Z., et al.: LLOP localization algorithm with optimal scaling in NLOS wireless propagations. In: Proceedings of the IEEE International Conference on Electronics Information and Emergency Communication, pp. 45–48 (2013)

    Google Scholar 

  22. Venkatraman, S., Caffery, J.J., You, H.R.: A novel TOA location algorithm using LOS range estimation for NLOS environments. IEEE Trans. Veh. Technol. 53(5), 1515–1524 (2004)

    Article  Google Scholar 

  23. Chan, Y.T., Ho, K.C.: A simple and efficient estimator for hyperbolic location. IEEE Trans. Signal Process. 42(8), 1905–1915 (1994)

    Article  Google Scholar 

  24. Wang, X., Wang, Z., O’Dea, B.: A TOA-based location algorithm reducing the errors due to non-line-of-sight (NLOS) propagation. IEEE Trans. Veh. Technol. 52(1), 112–116 (2003)

    Article  Google Scholar 

Download references

Acknowledgement

This paper was sponsored by the National Natural Science Foundation of China under grant No. 61471322.

Author information

Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Guangzhe Liu, Jingyu Hua, Feng Li, Weidang Lu & Zhijiang Xu

Authors
  1. Guangzhe Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingyu Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Feng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weidang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhijiang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jingyu Hua .

Editor information

Editors and Affiliations

  1. Zhejiang University of Technology, Hangzhou Shi, China

    Limin Meng

  2. Department of Networks, Simula Research Laboratory, Oslo, Norway

    Yan Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, G., Hua, J., Li, F., Lu, W., Xu, Z. (2018). An Improved Quadratic Programming LLOP Algorithm for Wireless Localization. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00557-3_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00556-6

  • Online ISBN: 978-3-030-00557-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation