IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v392y2021ics0096300320305683.html
   My bibliography  Save this article

Improved nonlinear observable degree analysis using data fusion

Author

Listed:
  • Ge, Quanbo
  • Tang, Shuaishuai
  • Wang, Mengmeng
  • Lu, Zhenyu

Abstract

For nonlinear Kalman filtering, process noise and observation noise affect the accuracy of system filtering, and filtering accuracy is related to observable degree. That is, the calculation of observable degree will be affected by process noise and observation noise. Traditional solutions for observable degree of nonlinear systems do not take noise into account. In this paper, the observable degree theory is solved by using the Cramer-Rao Lower Bound and the Lie derivative in differential geometry theory. The process noise and the observation noise are taken into account in the calculation matrix of the nonlinear observable degree. The paper proposes a new method based on condition number fusion. The iterative algorithm is used to make the condition number of the fused matrix reach the minimum value. In the result, the error of observable degree calculation can be reduced. The validity of the proposed method is verified by simulation, and the calculation theory of nonlinear observable degree is improved.

Suggested Citation

  • Ge, Quanbo & Tang, Shuaishuai & Wang, Mengmeng & Lu, Zhenyu, 2021. "Improved nonlinear observable degree analysis using data fusion," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    • Handle: RePEc:eee:apmaco:v:392:y:2021:i:c:s0096300320305683
    DOI: 10.1016/j.amc.2020.125613
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300320305683
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2020.125613?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.

    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:eee:apmaco:v:392:y:2021:i:c:s0096300320305683. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.