Brief paper: Distributed H∞-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case

  • Authors:

  • Bo Shen;Zidong Wang;Y. S. Hung

  • Affiliations:

  • School of Information Science and Technology, Donghua University, Shanghai 200051, China;School of Information Science and Technology, Donghua University, Shanghai 200051, China and Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, UK;Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2010

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Abstract

This paper is concerned with a new distributed H"~-consensus filtering problem over a finite-horizon for sensor networks with multiple missing measurements. The so-called H"~-consensus performance requirement is defined to quantify bounded consensus regarding the filtering errors (agreements) over a finite-horizon. A set of random variables are utilized to model the probabilistic information missing phenomena occurring in the channels from the system to the sensors. A sufficient condition is first established in terms of a set of difference linear matrix inequalities (DLMIs) under which the expected H"~-consensus performance constraint is guaranteed. Given the measurements and estimates of the system state and its neighbors, the filter parameters are then explicitly parameterized by means of the solutions to a certain set of DLMIs that can be computed recursively. Subsequently, two kinds of robust distributed H"~-consensus filters are designed for the system with norm-bounded uncertainties and polytopic uncertainties. Finally, two numerical simulation examples are used to demonstrate the effectiveness of the proposed distributed filters design scheme.