IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v45y2014i7p1393-1401.html
   My bibliography  Save this article

Stochastically resilient extended Kalman filtering for discrete-time nonlinear systems with sensor failures

Author

Listed:
  • Xin Wang
  • Edwin E. Yaz

Abstract

Missing sensor data is a common problem, which severely influences the overall performance of modern data-intensive control and computing applications. In order to address this important issue, a novel resilient extended Kalman filter is proposed for discrete-time nonlinear stochastic systems with sensor failures and random observer gain perturbations. The failure mechanisms of multiple sensors are assumed to be independent of each other with different failure rates. The locally unbiased robust minimum mean square filter is designed for state estimation under these conditions. The performance of the proposed estimation method is verified by means of numerical Monte Carlo simulation of two different nonlinear stochastic systems, involving a sinusoidal system and a Lorenz oscillator system.

Suggested Citation

  • Xin Wang & Edwin E. Yaz, 2014. "Stochastically resilient extended Kalman filtering for discrete-time nonlinear systems with sensor failures," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(7), pages 1393-1401, July.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:7:p:1393-1401
    DOI: 10.1080/00207721.2013.879257
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2013.879257
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2013.879257?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Joon B. Park & Xin Wang, 2018. "Sensorless Direct Torque Control of Surface-Mounted Permanent Magnet Synchronous Motors with Nonlinear Kalman Filtering," Energies, MDPI, vol. 11(4), pages 1-19, April.
    2. Tao Liu & Qiaoling Tong & Qiao Zhang & Qidong Li & Linkai Li & Zhaoxuan Wu, 2018. "A Method to Improve the Response of a Speed Loop by Using a Reduced-Order Extended Kalman Filter," Energies, MDPI, vol. 11(11), pages 1-16, October.

    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:45:y:2014:i:7:p:1393-1401. 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.