IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-0-8176-8238-5_6.html
   My bibliography  Save this book chapter

Adaptive Particle Filter for Stable Distribution

In: Integral Methods in Science and Engineering

Author

Listed:
  • H. F. de Campos Velho

    (Instituto Nacional de Pesquisas Espaciais (INPE))

  • H. C. Morais Furtado

    (Instituto Nacional de Pesquisas Espaciais (INPE))

Abstract

Particle filters are considered as the most robust method in the estimation theory. However, all techniques presented in the literature consider only cases where the data can be described by a probability distribution function (PDF) with all statistical moments well defined. The present chapter a novel particle filter is introduced for estimating a posteriori PDF using a Bayesian scheme. The key issue is to consider an adaptive likelihood function. The scheme generalizes the traditonal particle filter approaches. The scheme can be applied to inverse problem, control theory, image and signal processing, and data assimilation.

Suggested Citation

  • H. F. de Campos Velho & H. C. Morais Furtado, 2011. "Adaptive Particle Filter for Stable Distribution," Springer Books, in: Christian Constanda & Paul J. Harris (ed.), Integral Methods in Science and Engineering, edition 1, pages 47-57, Springer.
    • Handle: RePEc:spr:sprchp:978-0-8176-8238-5_6
    DOI: 10.1007/978-0-8176-8238-5_6
    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
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:sprchp:978-0-8176-8238-5_6. 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.