A hybrid indoor altimetry based on barometer and UWB

Si, M., Wang, Y., Zhou, N., Seow, C. and Siljak, H. (2023) A hybrid indoor altimetry based on barometer and UWB. Sensors, 23(9), 4180. (doi: 10.3390/s23094180)

Abstract

Accurate altimetry is essential for location-based services in commercial and industrial applications. However, current altimetry methods only provide low-accuracy measurements, particularly in multistorey buildings with irregular structures, such as hollow areas found in various industrial and commercial sites. This paper innovatively proposes a tightly coupled indoor altimetry system that utilizes floor identification to improve height measurement accuracy. The system includes two optimized algorithms that improve floor identification accuracy through activity detection and address the problem of difficult convergence of z-axis coordinates due to indoor coplanarity by applying constraints to iterative least squares (ILS). Two experiments were conducted in a teaching building and a laboratory, including an irregular environment with a hollow area. The results show that our proposed method for identifying floors based on activity detection outperforms other methods. In dynamic experiments, our method effectively eliminates repeated transformations during the up- and downstairs process, and in static experiments, it minimizes the impact of barometric drift. Furthermore, our proposed altimetry method based on constrained ILS achieves significantly improved positioning accuracy compared to ILS, 1D-CNN, and WC. Specifically, in the teaching building, our method achieves improvements of 0.84 m, 0.288 m, and 0.248 m, respectively, while in the laboratory, the improvements are 2.607 m, 0.696 m, and 0.625 m.

• Item Type: Articles
• Additional Information: This research was funded by the National Key Research and Development Program of China under Grant 2022YFE0102600, 2016YFB0502102, and by China Sponsorship Council grant number CSC 202106420051.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Seow, Dr Chee Kiat
• Authors: Si, M., Wang, Y., Zhou, N., Seow, C., and Siljak, H.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• ISSN (Online): 1424-8220
• Published Online: 22 April 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Sensors 23(9): 4180
• Publisher Policy: Reproduced under a Creative Commons License