Abstract
In this paper, we discuss the time-of-arrival (TOA) localization problem in the mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environment where the TOA measurements maybe corrupted by NLOS errors, which may severely degrade the localization accuracy. Under this condition, the LOS measurements are spatially consistent with source position, while the NLOS measurements corrupted by large and positive NLOS errors are always randomly divergent without consistency. Based on this principle, a soft-decision optimization method is developed to mitigate the NLOS errors. The proposed algorithm is heuristic, which can gradually highlight the LOS and suppress the NLOS in the optimization process along the consistent direction. It works well in mitigating NLOS errors, which can achieve the positioning accuracy close to that using only LOS measurements. The performance is analyzed from different aspects by simulations. The results show that the proposed algorithm performs better than the existing methods especially in the situation where NLOS error is large.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tabassum, N., Geetha, D.D.: Localization techniques in wireless sensor networks: a comprehensive survey. 43(8), (2021)
Sivasakthiselvan, S., Nagarajan, V.: Localization Techniques of Wireless Sensor Networks: A Review. In: 2020 International Conference on Communication and Signal Processing (ICCSP) (2020)
Ayaz, M., et al.: Wireless sensor's civil applications, prototypes, and future integration possibilities: a review[J]. IEEE Sensors J. 18(1), 4–30 (2018)
Tomic, S., Beko, M.: Exact robust solution to tw-toa-based target localization problem with clock imperfections[J]. IEEE Signal Process. Lett. 25(4), 531–535 (2018)
Zheng, Y., Sheng, M., Liu, J., Li, J.: Exploiting AoA estimation accuracy for indoor localization: a weighted AOA-based approach[J]. IEEE Wireless Commun. Lett. 8(1), 65–68 (2019)
Xi, L., Guo, F., Le, Y., Min, Z.: Improved solution for geolocating a known altitude source using TDOA and FDOA under random sensor location errors[J]. Electron. Lett. 54(9), 597–599 (2018)
Sun, Y., Yang, S., Wang, G., et al.: Robust RSS-based source localization with unknown model parameters in mixed LOS/NLOS environments[J]. IEEE Trans. Veh. Technol. 70(4), 3926–3931 (2021)
Zou, Y., Liu, H.: An efficient NLOS errors mitigation algorithm for TOA-based localization[J]. Sensors 20(5), 1403 (2020)
Chen, P.: A Non-line-of-sight error mitigation algorithm in location estimation[C]. In: Wireless Communications and Networking Conference, pp. 316–320 (1999)
Casas, R., Marco, A., Guerrero, J.J., Falco, J.L.: Robust estimator for non-line-of-sight error mitigation in indoor localization[J]. EURASIP J. Adv. Signal Process. 2006(1), 156–160 (2006)
Wang, X., Wang, Z., O'dea B. A TOA-based location algorithm reducing the errors due to non-line-of-sight (NLOS) propagation[J]. IEEE Trans. Veh. Technol. 52(1), 112–116 (2003)
Yang, K., An, J., Bu, X., Lu, Y.: A TOA-based location algorithm for NLOS environments using quadratic programming[C]. In: 2010 IEEE Wireless Communication and Networking Conference, pp. 1–5 (2010)
Wang, G., Chen, H., Li, Y., Ansari, N.: NLOS error mitigation for TOA-based localization via convex relaxation[J]. IEEE Trans. Wireless Commun. 13(8), 4119–4131 (2014)
Vaghefi R. M, Schloemann J, Buehrer R M. NLOS mitigation in TOA-based localization using semidefinite programming[C]. Workshop on Positioning Navigation and Communication, 1–6 (2013)
Tomic, S., Beko, M.: A robust NLOS bias mitigation technique for RSS-TOA-based target localization. IEEE Signal Process. Lett. 26(1), 64–68 (2019)
Chen, H., Wang, G., Ansari, N.: Improved robust TOA-based localization via NLOS balancing parameter estimation. IEEE Trans. Veh. Technol. 68(6), 6177–6181 (2019)
Guvenc, I., Chong, C.C.: A survey on TOA based wireless localization and NLOS mitigation techniques[J]. IEEE Commun. Surveys Tutorials 11(3), 107–124 (2009)
Geng, C., Yuan, X., Huang, H.: Exploiting channel correlations for NLOS ToA localization with multivariate Gaussian mixture models. IEEE Wireless Commun. Lett. 9(1), 70–77 (2020)
Shi, Q., Cui, X., Zhao, S., Lu, M.: Sequential TOA-based moving target localization in multi-agent networks. IEEE Commun. Lett. 24(8), 1719–1723 (2020)
Qi, Y., Kobayashi, H., Suda, H.: Analysis of wireless geolocation in a non-line-of-sight environment[J]. IEEE Trans. Wireless Commun. 5(3), 672–681 (2006)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Hofmann, T., Buhmann, J.M.: Pairwise data clustering by deterministic annealing. IEEE Trans. Pattern Anal. Mach. Intell. 19(1), 1–14 (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Beijing HIWING Sci. and Tech. Info Inst
About this paper
Cite this paper
Tian, Q., Liu, Y., Hu, Q. (2023). A NLOS Mitigation Algorithm for TOA Based Localization in Mixed LOS/NLOS Environments. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_263
Download citation
DOI: https://doi.org/10.1007/978-981-99-0479-2_263
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0478-5
Online ISBN: 978-981-99-0479-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)