Advanced Search
MyIDEAS: Login

Self-tuning weighted measurement fusion Kalman filtering algorithm

Contents:

Author Info

  • Ran, Chenjian
  • Deng, Zili
Registered author(s):

    Abstract

    For the multisensor linear discrete system with correlated noises and same measurement matrix, the self-tuning weighted measurement fusion Kalman filtering algorithm is presented when the model parameters and noise variances are all unknown. It can handle the self-tuning fused Kalman filtering, smoothing, and prediction problem and the input white noise deconvolution estimation problem. By the dynamic variance error system analysis (DVESA) method, it is proved that the solution of the self-tuning Riccati equation converges to the solution of the steady-state Riccati equation. Based on the convergence of the self-tuning Riccati equation, the convergence of the proposed self-tuning weighted measurement fusion Kalman estimator is proved. So it has asymptotic global optimality. Applying to the multi-channel autoregressive moving average (ARMA) signal with sensor bias, the corresponding self-tuning weighted measurement fusion Kalman estimator of the signal is also presented, where the estimates of unknown model parameters and noise variances are obtained by the multi-dimension recursive extended least squares (RELS) algorithm, the correlation method and the Gevers–Wouters algorithm with a dead band. One simulation example shows the effectiveness.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.sciencedirect.com/science/article/pii/S0167947312000035
    Download Restriction: Full text for ScienceDirect subscribers only.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 6 ()
    Pages: 2112-2128

    as in new window
    Handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:2112-2128

    Contact details of provider:
    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Multisensor measurement fusion; Correlated noises; Self-tuning Kalman filtering algorithm; Self-tuning Riccati equation convergence; ARMA signal;

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:2112-2128. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 profile, as there may be some citations waiting for confirmation.

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