Hybrid Data Fusion Method Using Bayesian Estimation and Fuzzy Cluster Analysis for WSN

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)

Abstract

Data fusion is the process of combining data from multiple sensors in order to minimize the amount of data and get an accurate estimation of the true value. The uncertainties in data fusion are mainly caused by two aspects, device noise and spurious measurement. This paper proposes a new fusion method considering these two aspects. This method consists of two steps. First, using fuzzy cluster analysis, the spurious data can be detected and separated from fusion automatically. Second, using Bayesian estimation, the fusion result is got. The superiorities of this method are the accuracy of the fusion result and the adaptability for occasions.

Keywords

Data fusion Fuzzy cluster analysis Bayesian estimation Spurious data 

Notes

Acknowledgments

This research is supported by National Natural Science Foundation of China under Grant 61071076, the National High-tech Research And Development Plans (863 Program) under Grant 2011AA010104-2, the Beijing Municipal Natural Science Foundation under Grant 4132057.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Huilei Fu
    • 1
    • 2
  • Yun Liu
    • 1
    • 2
  • Zhenjiang Zhang
    • 1
    • 2
  • Shenghua Dai
    • 1
  1. 1.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina
  2. 2.Key Laboratory of Communication and Information SystemsBeijing Municipal Commission of Education, Beijing Jiaotong UniversityBeijingChina

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