An Indoor Positioning Algorithm Based on Fingerprint and Mobility Prediction in RSS Fluctuation-Prone WLANs

Chun Han Lin, Lyu Han Chen, Hsiao Kuang Wu, Ming Hui Jin, Gen Huey Chen, Jose Luis Garcia Gomez, Cheng Fu Chou

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The creation of context-Aware services in pervasive computing environments has driven the wide development of wireless local area network (WLAN)-based indoor positioning systems. One of the main challenges in WLAN-based indoor positioning is the severe fluctuation of received signal strength (RSS), which may cause the RSS patterns to be mismatched and the positioning to be inaccurate. In this paper, an indoor positioning algorithm that combines the fingerprint scheme with mobility prediction is proposed. Since the mobility prediction is performed according to the moving speed and direction of the mobile client, the resulting location estimation is more stable compared to the use of RSS alone. Experimental results show that the proposed positioning algorithm can mitigate the impact of the RSS fluctuation and has better positioning accuracy and stability than previous fingerprint-based approaches.

Original languageEnglish
Article number8732597
Pages (from-to)2926-2936
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume51
Issue number5
DOIs
StatePublished - May 2021

Keywords

  • Fingerprint
  • indoor positioning
  • mobility prediction
  • received signal strength (RSS)
  • wireless local area network (WLAN)

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