Fingerprint and Assistant Nodes Based Wi-Fi Localization in Complex Indoor Environment
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- Qiyue Li
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, China
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- Wei Li
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, China
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- Wei Sun
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, China
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- Jie Li
- School of Computer and Information, Hefei University of Technology, Hefei, China
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- Zhi Liu
- Global Information and Telecommunication Institute, Waseda University, Tokyo, Japan
Description
With the extensive development of Wi-Fi, indoor location services based on received signal strength (RSS) fingerprints have attracted increasing attention from researchers. In complex indoor environments, multipath and non-line-of-sight (NLOS) conditions would lead to large errors in measured values, thereby reducing indoor positioning accuracy. In this paper, we propose a Wi-Fi indoor localization method based on collaboration of fingerprint and assistant nodes. First, appropriate assistant nodes based on the similarity of RSS sequences are elaborately selected around the unknown node and distances between them are used as auxiliary information to improve the positioning accuracy. Furthermore, in the complex indoor circumstances that result in NLOS error, an adaptive Kalman filter with colored noise is used to mitigate the time-of-flight ranging error. Experiments demonstrate that in complex indoor environments, our system can outperform its counterparts with robust performance and low localization estimation error.
Journal
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- IEEE Access
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IEEE Access 4 2993-3004, 2016
Institute of Electrical and Electronics Engineers (IEEE)
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Keywords
Details 詳細情報について
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- CRID
- 1360848660071184512
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- ISSN
- 21693536
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- Data Source
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- Crossref
- KAKEN
- OpenAIRE