Indoor localisation algorithm based on MSIA and FCSO. Issue 11 (1st July 2020)

• Record Type: Journal Article
• Authors: Li, Shirong; Fu, Maosheng; Zhu, Xuemei; Zhang, Fenghui; Chang, Xinyu; Zuo, Xukun; He, Fugui
• Is Part Of: IET communications. Volume 14:Issue 11(2020)
• Page Start: 1687
• Page End: 1695
• Publication Date: 2020-07-01
• Subjects: wireless LAN -- indoor radio -- iterative methods -- radionavigation -- Markov processes -- optimisation; structural optimisation stage -- fingerprints -- clustering optimisation stage -- positioning point -- offline workload -- indoor localisation algorithm -- wireless local area network indoor localisation method -- higher positioning accuracy -- time‐varying signal -- fingerprint clustering structural optimisation -- received signal strength time variation -- MSIA algorithm -- FCSO algorithm -- clustering optimisation stages; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2019.1187
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : 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.