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Hierarchical Asynchronous Fusion Estimation for WSNs

  • Wen-An Zhang
  • Bo Chen
  • Haiyu Song
  • Li Yu
Chapter
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Abstract

Distributed fusion is a typical structure for multisensor fusion estimation in WSNs, where the sensors generate local estimates ahead and then send them to a fusion center (FC) for fusion estimation [1, 2]. When the number of sensors is large, it is wasteful to embed in each sensor an estimator, and the FC requires a large bandwidth to communicate with the various sensors in a short time, which is usually impossible since the WSN is limited in bandwidth. An improvement is to adopt a hierarchical structure for fusion estimation [3–6]. In a hierarchical fusion estimation system, the sensors are divided into several clusters, and the sensors within the same cluster are connected to a local estimator. Moreover, only the local estimators are linked to the FC, and the measurements from sensors in a cluster are pretreated by local estimators in advance. A structure of the hierarchical fusion system is shown in Fig. 7.1. There are mainly two deficiencies in the existing hierarchical fusion estimation. First, local estimations and the fusion estimation are assumed to be time synchronized, which is restrictive as the processing rates of different clusters may be different from each other. Second, during the estimation interval, each sensor communicates with the local estimator only once, which implies that only one measurement from a sensor can be used for local estimation.

Keywords

Packet Loss Local Estimate Local Estimator Fusion Center Fusion Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bar-Shalom Y, Li XR (1990) Multitarget-multisensor tracking: advanced applications, vol 1. Artech House, NorwoodGoogle Scholar
  2. 2.
    Sun SL, Deng ZL (2004) Multi-sensor optimal information fusion Kalman filter. Automatica 40(6):1017–1023MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Ahmad A, Gani M, Yang F (2008) Decentralized robust Kalman filtering for uncertain stochastic systems over heterogeneous sensor networks. Signal Process 88(8):1919–1928CrossRefzbMATHGoogle Scholar
  4. 4.
    Fang J, Li H (2009) Power constrained distributed estimation with cluster-based sensor collaboration. IEEE Trans Wirel Commun 8(7):3822–3832CrossRefGoogle Scholar
  5. 5.
    Zhang P, Qi W, Deng Z (2015) Hierarchical fusion robust Kalman filter for clustering sensor network time-varying systems with uncertain noise variances. Int J Adapt Control Signal Process 29(1):99–122MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Chaudhary MH, Vandendorpe L (2013) Performance of power-constrained estimation in hierarchical wireless wensor networks. IEEE Trans Signal Process 61(3):724–739MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chen CT (1999) Linear systems theory and design, 3rd edn. Oxford University Press, New YorkGoogle Scholar
  8. 8.
    Julier SJ, Uhlman JK (2009) General decentralized data fusion with covariance intersection. In: Liggins ME, Hall DL, Llinas J (eds) Handbook of multisensor data fusion. Theory and practice, 2nd edn. CRC Press, Boca Raton, pp 319–343Google Scholar
  9. 9.
    Deng ZL, Zhang P, Qi W, Liu J, Gao Y (2012) Sequential covariance intersection fusion Kalman filter. Inf Sci 189(4):293–309MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Science Press, Beijing and Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Wen-An Zhang
    • 1
  • Bo Chen
    • 1
  • Haiyu Song
    • 2
  • Li Yu
    • 3
  1. 1.Department of AutomationZhejiang University of TechnologyHangzhouChina
  2. 2.Zhejiang Uni. of Finance & EconomicsHangzhouChina
  3. 3.Zhejiang University of TechnologyHangzhouChina

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