Si, M., Wang, Y., Seow, C. K. , Cao, H., Liu, H. and Huang, L. (2022) An adaptive weighted Wi-Fi FTM-based positioning method in an NLOS environment. IEEE Sensors Journal, 22(1), pp. 472-480. (doi: 10.1109/JSEN.2021.3124275)
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Abstract
In the field of indoor positioning, Wi-Fi FTM is a new technology for realizing high-precision positioning. However, errors caused by clock drift and non-line-of-sight (NLOS) signals affect its positioning accuracy. When receiving NLOS signals, most existing positioning algorithms only delete these signals, which decreases the number of nodes and may decrease accuracy. To address this issue, this paper proposed an adapted weighted positioning method under the NLOS environment. First, this method includes a compensation model to decrease the error caused by clock drift and multipath. Additionally, it can evaluate ranging results and improving the positioning accuracy by assigning greater weight to better ranging results. To verify the effectiveness and feasibility of the proposed method, a positioning experiment is performed under an NLOS environment. The results show that our proposed method is suitable for positioning in a completely NLOS environment and effectively improves the positioning accuracy. Compared with the traditional least squares-based method and the inverse distance weighting-based positioning method, the mean error of the proposed method outperformed by approximately 30% and 20% respectively.
| Item Type: | Articles |
|---|---|
| Additional Information: | This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0502102. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Seow, Dr Chee Kiat |
| Authors: | Si, M., Wang, Y., Seow, C. K., Cao, H., Liu, H., and Huang, L. |
| Subjects: | Q Science > QA Mathematics T Technology > T Technology (General) |
| College/School: | College of Science and Engineering > School of Computing Science |
| Research Centre: | College of Science and Engineering > School of Computing Science > IDA Section |
| Journal Name: | IEEE Sensors Journal |
| Publisher: | IEEE |
| ISSN: | 1530-437X |
| ISSN (Online): | 1558-1748 |
| Published Online: | 09 November 2021 |
| Copyright Holders: | Copyright © 2022 IEEE |
| First Published: | First published in IEEE Sensors Journal 22(1):472-480 |
| Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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