Distributed information consensus filters for simultaneous input and state estimation

Y. Lu, L Zhang, Xuerong Mao

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
155 Downloads (Pure)


This paper describes the distributed information filtering where a set of sensor networks are required to simultaneously estimate input and state of a linear discrete-time system from collaborative manner. Our research purpose is to develop a consensus strategy in which sensor nodes communicate within the network through a sequence of Kalman iterations and data diffusion. A novel recursive information filtering is proposed by integrating input estimation error into measurement data and weighted information matrices. On the fusing process, local system state filtering transmits estimation information using the consensus averaging algorithm, which penalizes the disagreement in a dynamic manner. A simulation example is provided to compare the performance of the distributed information filtering with optimal Gillijins–De Moor’s algorithm.
Original languageEnglish
Pages (from-to)877-888
Number of pages12
JournalCircuits, Systems, and Signal Processing
Issue number2
Publication statusPublished - 1 Apr 2013


  • unknown input estimation
  • distributed estimation
  • information filters
  • consensus
  • sensor networks

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