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A New Indoor Positioning Algorithm of Cellular and Wi-Fi Networks

Published online by Cambridge University Press:  11 December 2019

Meiling Chai ,
Changgeng Li  and
Hui Huang
Meiling Chai
Affiliation:
(School of Physics and Electronics, Central South University, Changsha, China)
Changgeng Li*
Affiliation:
(School of Physics and Electronics, Central South University, Changsha, China)
Hui Huang
Affiliation:
(School of Physics and Electronics, Central South University, Changsha, China)
*
(E-mail: lcgeng@csu.edu.cn)
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Abstract

Fluctuation of the received signal strength (RSS) is the key performance-limiting factor for Wi-Fi indoor positioning schemes. In this study, the Manhattan distance was used in the weighted K-nearest neighbour (WKNN) algorithm to improve positioning accuracy. Reference point (RP) intervals were optimised to reduce the complexity of the system. Specifically, two new positioning schemes are proposed in this paper. Scheme 1 uses the cellular network to refine the fingerprint database, while Scheme 2 uses the cellular network positioning to locate the node a priori, then uses the Wi-Fi network to further improve accuracy. The experimental results showed that the average positioning error of Scheme 1 was 1·60 m, a reduction of 12% compared with the existing Wi-Fi fingerprinting schemes. In Scheme 2, when double cellular networks were used, RP usage was reduced by 64% and the calculating time was 0·24 s, a reduction of up to 69·5% compared with the Manhattan-WKNN algorithm. These proposed schemes are suitable for high accuracy and real-time positioning situations, respectively.

Keywords

Hybrid NetworkIndoor PositioningThe WKNN AlgorithmCellular and Wi-Fi Networks
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 3 , May 2020 , pp. 509 - 529
Copyright
Copyright © The Royal Institute of Navigation 2019

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