Indoor localisation algorithm based on MSIA and FCSO

The wireless local area network indoor localisation method based on fingerprints has been widely researched and applied due to its higher positioning accuracy and lower cost. However, its engineering is limited because of the large offline workload and time-varying signal. To address these problems,...

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Bibliographic Details
Published in:IET communications 2020-07, Vol.14 (11), p.1687-1695
Main Authors: Li, Shirong, Fu, Maosheng, Zhu, Xuemei, Zhang, Fenghui, Chang, Xinyu, Zuo, Xukun, He, Fugui
Format: Article
Language:English
Subjects:
clustering optimisation stage
clustering optimisation stages
FCSO algorithm
fingerprint clustering structural optimisation
fingerprints
higher positioning accuracy
indoor localisation algorithm
indoor radio
iterative methods
Markov processes
MSIA algorithm
offline workload
optimisation
positioning point
radionavigation
received signal strength time variation
Research Article
structural optimisation stage
time‐varying signal
wireless LAN
wireless local area network indoor localisation method
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Summary:The wireless local area network indoor localisation method based on fingerprints has been widely researched and applied due to its higher positioning accuracy and lower cost. However, its engineering is limited because of the large offline workload and time-varying signal. To address these problems, an indoor localisation algorithm based on Markov state iterative analysis (MSIA) and fingerprint clustering structural optimisation (FCSO) is proposed in this study. First, the received signal strength time variation can be solved using the MSIA algorithm, which is based on the correlation of the memory source. Then, the offline workload can be considerably reduced and positioning accuracy can be improved with the FCSO algorithm, which contains fingerprint structural and clustering optimisation stages. In the structural optimisation stage, about half of the fingerprints can be omitted. In the clustering optimisation stage, high errors can be avoided. Finally, the locations of the positioning point can be obtained through the combination of MSIA and FCSO. Experimental results show the proposed algorithm can reduce the offline workload by about 50%, and the positioning accuracy can be increased when using MSIA and FCSO algorithms compared with other algorithms.
ISSN:1751-8628
1751-8636
1751-8636
DOI:10.1049/iet-com.2019.1187