Random weighting estimation for fusion of multi-dimensional position data

  • Authors:

  • Shesheng Gao;Yongmin Zhong;Bijan Shirinzadeh

  • Affiliations:

  • School of Automatics, Northwestern Polytechnical University, Xi'an, China;Department of Mechanical Engineering, Curtin University of Technology, Australia;Department of Mechanical and Aerospace Engineering, Monash University, Australia

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

This paper adopts the concept of random weighting estimation to multi-sensor data fusion. It presents a new random weighting estimation methodology for optimal fusion of multi-dimensional position data. A multi-sensor observation model is constructed for multi-dimensional position. Based on this observation model, a random weighting estimation algorithm is developed for estimation of position data from single sensors. Using the random weighting estimations from each single sensor, an optimization theory is established for optimal fusion of multi-sensor position data. Experimental results demonstrate that the proposed methodology can effectively fuse multi-sensor dimensional position data, and the fusion accuracy is much higher than that of the Kalman fusion method.