Abstract
In recent years, indoor positioning technology based on WIFI has been widely researched. However, traditional WIFI-based indoor positioning method can’t achieve high localization accuracy due to the clustering errors at some locations. In this paper, RSS and location based twice clustering (RLTC) and Multi-user Topology Approximation Algorithm is proposed. The algorithm is divided into two stages. RLTC method is proposed during offline stage to correct clustering results. During online stage, multi-user topology approximation method is proposed to reduce positioning error on some particular location. Experiments show that the proposed algorithms can effectively improve the positioning accuracy compared to traditional positioning method.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (61371127 & 61471361 & 61401330 & 61572389), the EU FP7 CROWN Project under Grant Number PIRSES-GA-2013-610524, the National High-Tech R&D Program (863 Program 2015AA01A705) and the 111 Project in Xidian University of China (B08038).
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Lu, X., Wang, J., Zhang, Z., Bian, H., Yang, E. (2017). WIFI-Based Indoor Positioning System with Twice Clustering and Multi-user Topology Approximation Algorithm. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-10-3966-9_30
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DOI: https://doi.org/10.1007/978-981-10-3966-9_30
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