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Author:

Zhang, Hui-Qing (Zhang, Hui-Qing.) | Xu, Jian-Bo (Xu, Jian-Bo.) | Zhang, Jing-Li (Zhang, Jing-Li.)

Indexed by:

EI Scopus

Abstract:

According to the existing problems in the WLAN fingerprint positioning technology and wireless signal propagation characteristics under indoor environment, this paper proposes a indoor location method with better rapidity and higher positioning accuracy. WLAN fingerprint positioning method is improved from three aspects in this article: first of all, through Kalman filtering, the data of signal fingerprints will be processed to eliminate the random disturbance and to represent the real state of the system; Second, by multi-fingerprints joint positioning method, the random error of individual fingerprint positioning will be offset, and the positioning precision and accuracy will be improved; Again, through the quick fingerprint matching method based on area partition and AP ID database, the fingerprint data need to match will be decreased multiply, the speed of matching fingerprint will be improved. The experimental verification confirmed that the algorithm or method proposed in this paper could improve the positioning accuracy and improve the speed of the WLAN fingerprint positioning system significantly. © 2014 IEEE.

Keyword:

Indoor positioning systems Kalman filters Wireless local area networks (WLAN) Pattern matching Location

Author Community:

  • [ 1 ] [Zhang, Hui-Qing]Academy of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Xu, Jian-Bo]Academy of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Zhang, Jing-Li]Academy of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China

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Source :

Year: 2014

Page: 4055-4059

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 4

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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