IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v51y2020i9p1669-1684.html
   My bibliography  Save this article

Data filtering-based parameter and state estimation algorithms for state-space systems disturbed by coloured noises

Author

Listed:
  • Ting Cui
  • Feng Ding
  • Ahmed Alsaedi
  • Tasawar Hayat

Abstract

In this paper, the combined parameter and state estimation issues of state-space systems are considered, and the process noises and observation noises are supposed to be coloured noises. By utilising the data filtering technique, we transform the original state-space system into the filtered system for eliminating the interference of the coloured noise in the state equation, and then we derive a filtering-based extended stochastic gradient (F-ESG) algorithm to estimate the system parameters. For estimating the unmeasurable states, we derive a new state estimator by using the preceding parameter estimates to take the place of the unknown system parameters in the Kalman filter. Furthermore, we propose a filtering-based multi-innovation extended stochastic gradient (F-MI-ESG) algorithm to achieve the higher parameter estimation accuracy. Finally, we provide two simulation examples to test and compare the performance of the proposed algorithms. The simulation results indicate that the F-ESG algorithm and the F-MI-ESG algorithm are effective for parameter estimation, and that the F-MI-ESG algorithm is able to achieve more accurate parameter estimates than the F-ESG algorithm.

Suggested Citation

  • Ting Cui & Feng Ding & Ahmed Alsaedi & Tasawar Hayat, 2020. "Data filtering-based parameter and state estimation algorithms for state-space systems disturbed by coloured noises," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(9), pages 1669-1684, July.
  • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:9:p:1669-1684
    DOI: 10.1080/00207721.2020.1772403
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2020.1772403
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207721.2020.1772403?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:51:y:2020:i:9:p:1669-1684. 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.