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Optimal consensus-based distributed estimation with intermittent communication

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  • Wen Yang
  • Xiaofan Wang
  • Hongbo Shi

Abstract

In this article, we consider the problem of distributed state estimation over a wireless sensor network (WSN). We firstly propose a distributed algorithm to estimate the state of a system by modelling the WSN as a directed network. Based on the Kalman filter, we introduce a consensus scheme for each sensor by including the estimates received from its neighbour sensors. We also consider intermittent and random data packet drops which are frequently seen in wireless networks. A sufficient condition is derived for the convergence of the state estimation error, and a upper bound is obtained for the estimation error covariance. We further consider minimising the estimation error by finding an optimal consensus gain for a given fixed network. The performance and effectiveness of the proposed algorithm are compared with existing well-known results from the literature.

Suggested Citation

  • Wen Yang & Xiaofan Wang & Hongbo Shi, 2011. "Optimal consensus-based distributed estimation with intermittent communication," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(9), pages 1521-1529.
    • Handle: RePEc:taf:tsysxx:v:42:y:2011:i:9:p:1521-1529
    DOI: 10.1080/00207721.2011.565135
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    Cited by:

    1. Donato Di Paola & Antonio Petitti & Alessandro Rizzo, 2015. "Distributed Kalman filtering via node selection in heterogeneous sensor networks," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(14), pages 2572-2583, October.

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