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A new method for improving Wi-Fi-based indoor positioning accuracy


Bai, Yuntian Brian; Wu, Suqin; Retscher, Guenther; Kealy, Allison; Holden, Lucas; Tomko, Martin; Borriak, Aekarin; Hu, Bin; Wu, Hong Ren; Zhang, Kefei (2014). A new method for improving Wi-Fi-based indoor positioning accuracy. Journal of Location Based Services:online.

Abstract

Wi-Fi- and smartphone-based positioning technologies are playing a more and more important role in location-based service industries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To address this problem, a new method for improving the indoor positioning accuracy was developed. The new method initially used the nearest neighbour (NN) algorithm of the fingerprinting method to identify the initial position estimate of the smartphone user. Then two distance correction values in two roughly perpendicular directions were calculated by the path loss model based on the two signal strength indicator values observed. The systematic error from the path loss model were eliminated by differencing two model-derived distances from the same access point. The new method was tested and the results compared and assessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy improved to 3.3 m from 3.8 m compared with the NN algorithm.

Abstract

Wi-Fi- and smartphone-based positioning technologies are playing a more and more important role in location-based service industries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To address this problem, a new method for improving the indoor positioning accuracy was developed. The new method initially used the nearest neighbour (NN) algorithm of the fingerprinting method to identify the initial position estimate of the smartphone user. Then two distance correction values in two roughly perpendicular directions were calculated by the path loss model based on the two signal strength indicator values observed. The systematic error from the path loss model were eliminated by differencing two model-derived distances from the same access point. The new method was tested and the results compared and assessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy improved to 3.3 m from 3.8 m compared with the NN algorithm.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2014
Deposited On:25 Nov 2014 16:01
Last Modified:08 Dec 2017 08:18
Publisher:Taylor & Francis Inc.
ISSN:1748-9725
Publisher DOI:https://doi.org/10.1080/17489725.2014.977362

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