Two-level Indoor Navigation using WiFi and MEMS Sensors

DOI

10.2991/mmsa-18.2018.51How to use a DOI?

Keywords

indoor navigation, MEMS sensors, WiFi, extended kalman filter

Abstract

In this paper, the indoor pedestrian location algorithm is investigated, in which we proposes a novel WiFi/MEMS integration structure for indoor navigation to fuse the information from WiFi and MEMS sensors. In WiFi part, aiming at the problem of large amount of calculation in the algorithm, a partition solution called “moving partition” is originally proposed. The test results from indoor experiments indicate that with “moving partition”, the computation time decreased by almost half and the proposed WiFi/MEMS solution works well, of which the mean positioning accuracy has been greatly improved compared with MEMS alone and WiFi alone.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yuliang Huang
AU  - Yongbo Zhang
AU  - Yi Cui
AU  - Zhihua Wang
AU  - Huimin Fu
PY  - 2018/03
DA  - 2018/03
TI  - Two-level Indoor Navigation using WiFi and MEMS Sensors
BT  - Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)
PB  - Atlantis Press
SP  - 226
EP  - 228
SN  - 1951-6851
UR  - https://doi.org/10.2991/mmsa-18.2018.51
DO  - 10.2991/mmsa-18.2018.51
ID  - Huang2018/03
ER  -