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A distributed Kalman filtering algorithm with a fast convergence rate for networked systems with multiplicative noise.

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Title: A distributed Kalman filtering algorithm with a fast convergence rate for networked systems with multiplicative noise.
Authors: Yang, Qiqi1 (AUTHOR) yangqiqi23@sdjzu.edu.cn, Zhang, Huanshui2 (AUTHOR), Fu, Minyue3 (AUTHOR) brianfu2106@outlook.com
Source: Asian Journal of Control. May2026, Vol. 28 Issue 3, p1396-1409. 14p.
Subject Terms: *Kalman filtering, *Message passing (Computer science), *Directed acyclic graphs, *Signal-to-noise ratio, *Graph theory, *Multiagent systems
Abstract: This article studies a distributed Kalman filtering (DKF) algorithm with a fast convergence rate, based on a networked system with multiplicative noise. Each node involved in the distributed estimation can independently compute the optimal state estimate using its local measurements and by exchanging information with neighboring nodes. First, a DKF algorithm is proposed based on the idea of a message‐passing algorithm. Subsequently, the convergence of this DKF algorithm in a finite number of steps for acyclic graphs is given. Moreover, the equivalence between cyclic and acyclic graphs is demonstrated using the depth‐first search algorithm, thereby illustrating its convergence for general topological graphs. Finally, three examples are provided to demonstrate that this distributed algorithm can converge to the central Kalman filter estimate. Compared to other distributed algorithms, it exhibits faster convergence. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Availability: 0
Header DbId: enr
DbLabel: Energy & Power Source
An: 193710639
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A distributed Kalman filtering algorithm with a fast convergence rate for networked systems with multiplicative noise.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Qiqi%22">Yang, Qiqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yangqiqi23@sdjzu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Huanshui%22">Zhang, Huanshui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fu%2C+Minyue%22">Fu, Minyue</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> brianfu2106@outlook.com</i>
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Asian+Journal+of+Control%22">Asian Journal of Control</searchLink>. May2026, Vol. 28 Issue 3, p1396-1409. 14p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br />*<searchLink fieldCode="DE" term="%22Message+passing+%28Computer+science%29%22">Message passing (Computer science)</searchLink><br />*<searchLink fieldCode="DE" term="%22Directed+acyclic+graphs%22">Directed acyclic graphs</searchLink><br />*<searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br />*<searchLink fieldCode="DE" term="%22Graph+theory%22">Graph theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This article studies a distributed Kalman filtering (DKF) algorithm with a fast convergence rate, based on a networked system with multiplicative noise. Each node involved in the distributed estimation can independently compute the optimal state estimate using its local measurements and by exchanging information with neighboring nodes. First, a DKF algorithm is proposed based on the idea of a message‐passing algorithm. Subsequently, the convergence of this DKF algorithm in a finite number of steps for acyclic graphs is given. Moreover, the equivalence between cyclic and acyclic graphs is demonstrated using the depth‐first search algorithm, thereby illustrating its convergence for general topological graphs. Finally, three examples are provided to demonstrate that this distributed algorithm can converge to the central Kalman filter estimate. Compared to other distributed algorithms, it exhibits faster convergence. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193710639
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/asjc.3746
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 1396
    Subjects:
      – SubjectFull: Kalman filtering
        Type: general
      – SubjectFull: Message passing (Computer science)
        Type: general
      – SubjectFull: Directed acyclic graphs
        Type: general
      – SubjectFull: Signal-to-noise ratio
        Type: general
      – SubjectFull: Graph theory
        Type: general
      – SubjectFull: Multiagent systems
        Type: general
    Titles:
      – TitleFull: A distributed Kalman filtering algorithm with a fast convergence rate for networked systems with multiplicative noise.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Yang, Qiqi
      – PersonEntity:
          Name:
            NameFull: Zhang, Huanshui
      – PersonEntity:
          Name:
            NameFull: Fu, Minyue
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
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              Value: 15618625
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            – Type: volume
              Value: 28
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Asian Journal of Control
              Type: main
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