RWKNN: A Modified WKNN Algorithm Specific for the Indoor Localization Problem

This paper proposes a restricted weighted <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-nearest neighbor algorithm (RWKNN) specific for indoor environments. The traditional WKNN method determines the locations by calculating the differ...

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Veröffentlicht in:IEEE sensors journal Jg. 22; H. 7; S. 7258 - 7266
Hauptverfasser: Chen, Guokai, Guo, Xiye, Liu, Kai, Li, Xiaoyu, Yang, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k -nearest neighbor
Algorithms
Ambiguity
Datasets
Fingerprint recognition
Fingerprint-based localization
Indoor environments
indoor positioning
Iterative methods
Location awareness
Measurement uncertainty
received signal strength
Robustness (mathematics)
Sensors
Signal strength
Trajectory
Wireless fidelity
ISSN:1530-437X, 1558-1748
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Zusammenfassung:This paper proposes a restricted weighted <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-nearest neighbor algorithm (RWKNN) specific for indoor environments. The traditional WKNN method determines the locations by calculating the difference between the current received signal strength (RSS) and fingerprint RSS. However, the limitations of the traditional WKNN positioning method include RSS instability and spatial ambiguity. With this focus, the proposed RWKNN considers indoor moving constraints and uses searching rectangular and trajectory restriction to reduce spatial ambiguity. In addition, to mitigate the effects of RSS instability on the iteration-based method, a confidence number is introduced. Through simulation of office environments, field experiments, and verification based on public datasets, numerical results show the superiority and effectiveness of the proposed RWKNN over other constraint-based algorithms in terms of robustness and accuracy. Specifically, RWKNN outperformed the traditional WKNN method by 43% and 20% in the simulation and field experiment tests respectively, and by 22% on the Tampere open dataset.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3155902