Distributed Consensus Kalman Filtering Over Time-Varying Graphs

Aviv Priel, Daniel Zelazo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes an improved method for distributed consensus Kalman filtering (DCKF). We introduce a minor modification to the consensus kalman filter proposed in (Olfati-Saber (2009)). Namely an extra averaging term is introduced into the filter update equations. In this direction, we propose a decentralized consensus gain that can be computed by each agent in the sensor network, and depends only on local properties of the network, i.e., the number of neighbors of each sensor. Moreover we prove that this scheme is stable for networks with time varying communication regime. Our results are compared to other existing solutions in the literature with a numerical example.

Original languageEnglish
Title of host publicationIFAC-PapersOnLine
EditorsHideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
PublisherElsevier B.V.
Pages10228-10233
Number of pages6
Edition2
ISBN (Electronic)9781713872344
DOIs
StatePublished - 1 Jul 2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023

Publication series

NameIFAC-PapersOnLine
Number2
Volume56

Conference

Conference22nd IFAC World Congress
Country/TerritoryJapan
CityYokohama
Period9/07/2314/07/23

Keywords

  • Cooperative systems
  • Distributed control and estimation
  • distributed Kalman filtering
  • Estimation and filtering
  • Sensor networks

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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