Gaussian-consensus filter for nonlinear systems with randomly delayed measurements in sensor networks

Yanbo Yang, Yan Liang, Quan Pan, Yuemei Qin, Xiaoxu Wang

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

This paper presents the decentralized state estimation problem of discrete-time nonlinear systems with randomly delayed measurements in sensor networks. In this problem, measurement data from the sensor network is sent to a remote processing network via data transmission network, with random measurement delays obeying a Markov chain. Here, we present the Gaussian-consensus filter (GCF) to pursue a tradeoff between estimate accuracy and computing time. It includes a novel Gaussian approximated filter with estimated delay probability (GEDPF) online in the sense of minimizing the estimate error covariance in each local processing unit (PU), and a consensus strategy among PUs in processing network to give a fast decentralized fusion. A numerical example with different measurement delays is simulated to validate the proposed method.

Original languageEnglish
Pages (from-to)91-102
Number of pages12
JournalInformation Fusion
Volume30
DOIs
StatePublished - 1 Jul 2016

Keywords

  • Consensus filter
  • Gaussian approximated filter
  • Markov chain
  • Randomly delayed measurements
  • Sensor networks

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