Abstract
The non-line-of-sight (NLOS) error is a major error source in wireless localization. Therefore, an improved constrained least-squares (CLS) algorithm is put forward to tackle this issue, where the positioning problem is formulated as a mathematical programming problem. And then, the cost function of the optimization is studied and a new one is proposed. Finally, through the presented optimization, we try to minimize the positioning influence of NLOS errors. Moreover, the studied method does not depend on a particular distribution of the NLOS error. Simulation results show that the positioning accuracy is significantly improved over traditional CLS algorithms, even under highly NLOS conditions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Yan, Y.: Efficient convex optimization method for underwater passive source localization based on RSS with WSN. In: Proceedings of the 2012 IEEE International Conference on Signal Processing, Communication and Computing, pp. 171–174 (2012)
Gong, X., Ning, Z., Guo, L., et al.: Location-recommendation-aware virtual network embedding in energy-efficient optical-wireless hybrid networks supporting 5G models. IEEE Access 4, 3065–3075 (2016)
Gong, X., Zhang, Q., Guo, L.: Optical-wireless hybrid virtual network embedding based on location recommendations. In: International Conference on Transparent Optical Networks, pp. 1–4. IEEE (2016)
Wu, N., Xiong, Y., Wang, H., et al.: A performance limit of TOA-based location-aware wireless networks with ranging outliers. IEEE Commun. Lett. 19(8), 1414–1417 (2015)
Ke, W., Wu, L.: Constrained least squares algorithm for TOA-based mobile location under NLOS environments. In: International Conference on Wireless Communications, NETWORKING and Mobile Computing, pp. 1–4. IEEE (2009)
Chan, Y.T., Hang, H.Y.C., Ching, P.C.: Exact and approximate maximum likelihood localization algorithms. IEEE Trans. Veh. Technol. 55(1), 10–16 (2006)
Zhang, J., Dong, F., Feng, G., et al.: Analysis of the NLOS channel environment of TDOA multiple algorithms. In: Sensors, pp. 1–4. IEEE (2016)
Zhong, Z., Jeong, J., Zhu, T., et al.: Node localization in wireless sensor networks. In: Handbook on Sensor Networks, pp. 535–563 (2014)
Zhang, S., Gao, S., Wang, G., et al.: Robust NLOS error mitigation method for TOA-based localization via second-order cone relaxation. IEEE Commun. Lett. 19(12), 2210–2213 (2015)
Li, X., Cai, X., Hei, Y., Yuan, R.: NLOS identification and mitigation based on channel state information for indoor WiFi localization. IET Commun. 11(4), 531–537 (2017)
Zheng, X., Hua, J., Zheng, Z., Zhou, S., Jiang, B.: LLOP localization algorithm with optimal scaling in NLOS wireless propagations. In: Proceedings of the 2013 IEEE 4th International Conference on Electronics Information and Emergency Communication, Beijing, pp. 45–48 (2013)
Li, X., Cao, F.: Location based TOA algorithm for UWB wireless body area networks. In: IEEE, International Conference on Dependable, Autonomic and Secure Computing, pp. 507–511. IEEE (2014)
Wang, X., Wang, Z., O’Dea, B.: A TOA-based location algorithm reducing the errors due to non-line-of-sight (NLOS) propagation. J. China Inst. Commun. 52(1), 112–116 (2003)
Zheng, X., Hua, J., Zheng, Z., et al.: LLOP localization algorithm with optimal scaling in NLOS wireless propagations. In: IEEE International Conference on Electronics Information and Emergency Communication, pp. 45–48. IEEE (2014)
Wu, S., Li, J., Liu, S., et al.: Improved and extended range scale algorithm for wireless cellular location. IEEE Commun. Lett. 16(2), 196–198 (2012)
Acknowledgement
This paper was sponsored by the National NSF of China No.61471322.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yin, Y., Hua, J., Chen, F., Lu, W., Wang, D., Li, J. (2018). An Improved Constrained Least Squares Localization Algorithm in NLOS Propagating Environment. 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_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-00557-3_9
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)