Skip to main navigation Skip to search Skip to main content

WLAN indoor tracking method via improved particle filter algorithm

  • Yubin Xu*
  • , Jingyu Liu
  • , Lin Ma
  • , Lang Peng
  • *Corresponding author for this work
  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

WLAN Indoor tracking system is presented based on the comparison between the off-line pre-stored Radio-map and new recorded signal strength in the on-line phase to estimate user's motion trajectory. Furthermore, the improved particle filter tracking algorithm that consists of the particles-reference points (P-RPs) transferring for getting the likelihood function and velocity estimation from the ANN positioning results is also discussed in this paper. And also, the experiment shows that this improved particle filter tracking algorithm achieves great accuracy performance in tracking trajectory aspect without any velocity-measurement hardware. Finally, the feasibility and effectiveness of this improved WLAN indoor particle filter tracking algorithm are verified without velocity-measurement hardware.

Original languageEnglish
Title of host publicationProceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
Pages1078-1082
Number of pages5
DOIs
StatePublished - 2010
Event1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010 - Harbin, China
Duration: 17 Sep 201019 Sep 2010

Publication series

NameProceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010

Conference

Conference1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
Country/TerritoryChina
CityHarbin
Period17/09/1019/09/10

Keywords

  • P-RPs
  • Particle filter
  • Tracking
  • Velocity estimation
  • WLAN

Fingerprint

Dive into the research topics of 'WLAN indoor tracking method via improved particle filter algorithm'. Together they form a unique fingerprint.

Cite this