IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v53y2022i3p575-592.html
   My bibliography  Save this article

Distributed Kalman filtering for sensor networks with random sensor activation, delays, and packet dropouts

Author

Listed:
  • Hao Jin
  • Shuli Sun

Abstract

This paper studies a distributed Kalman filtering problem for sensor networks, where sensor nodes may suffer from measuring the target state with a random activation nature and random delayed and lost state estimates of neighbour nodes due to unreliability of communication links. A distributed Kalman filter (DKF) is proposed, where predictor compensations for delayed and lost estimates of neighbour nodes and different consensus filter gains for state estimates of different neighbour nodes are used to improve estimation accuracy. Optimal filter gains with optimal parameters are designed to obtain a local minimum upper bound of filtering error covariance matrix, where optimal filter gains include an optimal Kalman filter gain for each sensor node and optimal multi-consensus filter gains for state estimates of its neighbour nodes. Our proposed DKF has a low computational cost because the calculation of cross-covariance matrices between estimates of sensor nodes is avoided. Besides, the boundedness of the proposed DKF is analysed. Finally, an example of a target tracking system in sensor networks demonstrates effectiveness of the proposed DKF.

Suggested Citation

  • Hao Jin & Shuli Sun, 2022. "Distributed Kalman filtering for sensor networks with random sensor activation, delays, and packet dropouts," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(3), pages 575-592, February.
    • Handle: RePEc:taf:tsysxx:v:53:y:2022:i:3:p:575-592
    DOI: 10.1080/00207721.2021.1963502
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2021.1963502
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2021.1963502?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tsysxx:v:53:y:2022:i:3:p:575-592. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.