Volume XXXIX-B1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B1, 503-508, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B1-503-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B1, 503-508, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B1-503-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  27 Jul 2012

27 Jul 2012

A LOCATION-TRACKING TESTBED USING VISION-ASSISTED SCHEME FOR WIRELESS SENSOR NETWORKS

F. Tsai1,2, Y. S. Chiou1, and H. Chang2 F. Tsai et al.
  • 1Center for Space and Remote Sensing Research, National Central University, Jhongda Rd., Jhongli, Taiwan
  • 2Department of Civil Engineering, National Central University, Jhongda Rd., Jhongli, Taiwan

Keywords: Alpha-Beta Filtering, Kalman Filtering, Location Tracking, Normalized Cross Correlation, WSN

Abstract. This paper presents the performance of an efficient location tracking algorithm based on Alpha-Beta (α-β) filtering with vision-assisted in a wireless sensor network (WSN) environment. With a vision-assisted calibration technique based on normalized cross-correlation 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 vision-assisted 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.