Skip to main content
SpringerLink
Log in

Research on a factor graph-based robust UWB positioning algorithm in NLOS environments

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

In a non-line-of-sight (NLOS) environment, ultra-wide band (UWB) high accuracy positioning has been one of the hot topics in studying indoor positioning. In this paper, a factor graph-based UWB positioning algorithm has been proposed based on an improved Turkey robust kernel. It has overcome not only the defect of the least squares algorithm for UWB positioning against non-Gaussian noise but also eliminated the shortcoming of Turkey robust kernel against over-optimization. Aiming at the character of UWB data generally larger than its true value due to barriers, robust kernel will be added into merely big ranging data. However, due to the presence of small ranging data possibly caused by error positioning, the squares of residuals will be taken as the optimized objective function. The experimental result proves that UWB positioning algorithm based on the improved Turkey robust kernel outperforms ordinary UWB positioning algorithms in NLOS environments, with the average positioning accuracy improved by around 20–30%.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Santoso, F., & Redmond, S. J. (2015). Indoor location-aware medical systems for smart homecare and telehealth monitoring: State-of-the-art. Physiological Measurement, 36(10), R53.

    Article  Google Scholar 

  2. García, E., Poudereux, P., Álvaro Hernández, Ureña, J., & Gualda, D. (2015). A robust UWB indoor positioning system for highly complex environments. In IEEE international conference on industrial technology (pp. 3386–3391). IEEE.

  3. Zhang, J., & Shen, C. (2016). Research on UWB indoor positioning in combination with TDOA improved algorithm and Kalman filtering. Modern Electronics Technique, 39(13), 1–5.

    Google Scholar 

  4. Fan, Q., Sun, B., Sun, Y., & Zhuang, X. (2017). Performance enhancement of Mems-based INS/UWB integration for indoor navigation applications. IEEE Sensors Journal, 17(10), 3116–3130.

    Article  Google Scholar 

  5. Sczyslo, S., Schroeder, J., Galler, S., & Kaiser, T. (2008). Hybrid localization using UWB and inertial sensors. In IEEE international conference on ultra-wideband (Vol. 3, pp. 89–92). IEEE Xplore.

  6. Xu, Y., & Chen, X. (2016). Range-only UWB/INS tightly integrated navigation method for indoor pedestrian. Chinese Journal of Scientific Instrument, 37(8), 142–148.

    Google Scholar 

  7. Shao-Hua, W. U., Zhang, Q. Y., & Zhang, N. T. (2008). A NLOS error mitigation method for UWB ranging in dense multi-path environments. Acta Electronica Sinica, 36(1), 39–45.

    Google Scholar 

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

    Article  Google Scholar 

  9. Benedetto, F., Giunta, G., Toscano, A., & Vegni, L. (2007). Dynamic LOS/NLOS statistical discrimination of wireless mobile channels. In IEEE vehicular technology conference. IEEE.

  10. Conti, A., Guerra, M., Dardari, D., Decarli, N., & Win, M. Z. (2012). Network experimentation for cooperative localization. IEEE Journal on Selected Areas in Communications, 30(2), 467–475.

    Article  Google Scholar 

  11. Benini, A., Mancini, A., & Longhi, S. (2013). An IMU/UWB/vision-based extended Kalman filter for mini-uav localization in indoor environment using 802.15.4a wireless sensor network. Journal of Intelligent & Robotic Systems, 70(1), 461–476.

    Article  Google Scholar 

  12. Wang, X., Hoseinnezhad, R., Gostar, A. K., Rathnayake, T., Xu, B., & Bab-Hadiashar, A. (2018). Multi-sensor control for multi-object bayes filters. Signal Processing, 142, 260–270.

    Article  Google Scholar 

  13. Wang, Y., & Li, X. (2017). The IMU/UWB fusion positioning algorithm based on a particle filter. ISPRS International Journal of Geo-Information, 6(8), 235.

    Article  Google Scholar 

  14. Blanco, J. L., Galindo, C., Ortiz-De-Galisteo, A., & Moreno, F. A. (2009). Mobile robot localization based on ultra-wide-band ranging: A particle filter approach. Robotics & Autonomous Systems, 57(5), 496–507.

    Article  Google Scholar 

  15. Wymeersch, H. (2010). NLOS identification and mitigation for UWB localization based on experimental data. IEEE Journal on Selected Areas in Communications, 28(7), 1026–1035.

    Article  Google Scholar 

  16. Xiao, Z., Wen, H., Markham, A., Trigoni, N., Blunsom, P., & Frolik, J. (2013). Identification and mitigation of non-line-of-sight conditions using received signal strength. In IEEE international conference on wireless and mobile computing.

  17. Yu, K., Wen, K., Li, Y., Zhang, S., & Zhang, K. (2018). A novel NLOS mitigation algorithm for UWB localization in harsh indoor environments. IEEE Transactions on Vehicular Technology, 68(1), 1–1.

    Google Scholar 

  18. Xu, Y., Chen, X., Cheng, J., Zhao, Q., & Wang, Y. (2016). Improving tightly-coupled model for indoor pedestrian navigation using foot-mounted IMU and UWB measurements. In Instrumentation and measurement technology conference proceedings. IEEE.

  19. Fan, Q., Wu, Y., Hui, J., Wu, L., Yu, Z., & Zhou, L. (2014). Integrated navigation fusion strategy of INS/UWB for indoor carrier attitude angle and position synchronous tracking. Scientific World Journal, 2014, 215303.

  20. Johnson, M. E., & Sathyan, T. (2011). Improved orientation estimation in complex environments using low-cost inertial sensors. In Proceedings of the, international conference on information fusion (pp. 1–8). IEEE.

  21. Hol, J. D., Dijkstra, F., Luinge, H., & Schon, T. B. (2009). Tightly coupled UWB/IMU pose estimation. In IEEE international conference on ultra-wideband (pp. 688–692).

  22. Savioli, A., Goldoni, E., Savazzi, P., & Gamba, P. (2013). Low complexity indoor localization in wireless sensor networks by UWB and inertial data fusion. Computer Science, 52(4), 723–732.

    Google Scholar 

  23. Ascher, C., Zwirello, L., Zwick, T., & Trommer, G. (2011). Integrity monitoring for UWB/INS tightly coupled pedestrian indoor scenarios. In International conference on indoor positioning and indoor navigation (pp. 1–6).

  24. Kaess, M., Ranganathan, A., & Dellaert, F. (2007). iSAM: Fast incremental smoothing and mapping with efficient data association. In Proceedings 2007 IEEE international conference on robotics and automation. IEEE.

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 41674030 and China Postdoctoral Science Foundation under Grant No. 2016M601909 and the grand of China Scholarship Council.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Xin Li & Yang Wang

  2. Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, Xuzhou, China

    Xin Li

Authors
  1. Xin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Li.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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

Li, X., Wang, Y. Research on a factor graph-based robust UWB positioning algorithm in NLOS environments. Telecommun Syst 76, 207–217 (2021). https://doi.org/10.1007/s11235-020-00709-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-020-00709-2

Keywords

Search

Navigation