IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-38391-5_132.html
   My bibliography  Save this book chapter

Research on Bayesian Estimation of Time-Varying Delay

In: The 19th International Conference on Industrial Engineering and Engineering Management

Author

Listed:
  • Meng Wang

    (University of Beijing Transportation)

  • Ying Liu

    (University of Beijing Transportation)

  • Ji-wang Zhang

    (University of Beijing Transportation)

Abstract

Time delay estimation is one of key techniques to array signal processing, and it has already had several mature algorithms. According to its different scenes, time delay estimation can be transferred to the estimation of coefficients of adaptive filter, which is on the basis of parameter model of adaptive filter. The simulations of Bayesian methods including Extended Kalman Filter, Unscented Kalman Filter and Bootstrap Particle Filter show that under Gaussian nonlinear system, EKF and UKF can estimate time-varying delay effectively. Besides, algorithms of UKF perform better than that of EKF, which are only subject to Gaussian system. In the nonlinear non-Gaussian system, BSPF is able to estimate time delay exactly.

Suggested Citation

  • Meng Wang & Ying Liu & Ji-wang Zhang, 2013. "Research on Bayesian Estimation of Time-Varying Delay," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), The 19th International Conference on Industrial Engineering and Engineering Management, edition 127, chapter 0, pages 1251-1262, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-38391-5_132
    DOI: 10.1007/978-3-642-38391-5_132
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-3-642-38391-5_132. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.