A location-tracking testbed using vision-assisted scheme for wireless sensor networks

F. Tsai, Y. S. Chiou, H. Chang

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


This paper presents the performance of an efficient location tracking algorithm based on Alpha-Beta (α-β) filtering with visionassisted in a wireless sensor network (WSN) environment. With a vision-assisted calibration technique based on normalized crosscorrelation scheme, the proposed approach is an accuracy enhancement procedure that effectively removes system errors causing uncertainty in measuring a dynamic environment. That is, using the vision-assisted approach to estimate the locations of the reference nodes as landmarks, an α-β tracking scheme with the landmark information can calibrate the location estimation and improve the corner effect. The experimental results demonstrate that the proposed location-tracking algorithm combining visionassisted scheme with α-β filtering approach can achieve an accurate location very close to the traditional Kalman filtering (KF) algorithm in a ZigBee positioning platform. As compared with the KF-based approach, the proposed tracking approach can avoid repeatedly calculating the Kalman gain and achieve reasonably good performance with much lower computational complexity.

Original languageEnglish
Pages (from-to)503-508
Number of pages6
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
StatePublished - 2012
Event22nd Congress of the International Society for Photogrammetry and Remote Sensing, ISPRS 2012 - Melbourne, Australia
Duration: 25 Aug 20121 Sep 2012


  • Alpha-beta filtering
  • Kalman filtering
  • Location tracking
  • Normalized cross correlation
  • WSN


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