IDEAS home Printed from https://ideas.repec.org/h/ito/pchaps/122365.html
   My bibliography  Save this book chapter

Nonlinear Filtering of Weak Chaotic Signals

In: Chaos Theory

Author

Listed:
  • Valeri Kontorovich
  • Zinaida Lovtchikova
  • Fernando Ramos-Alarcon

Abstract

In recent years, the application of nonlinear filtering for processing chaotic signals has become relevant. A common factor in all nonlinear filtering algorithms is that they operate in an instantaneous fashion, that is, at each cycle, a one moment of time magnitude of the signal of interest is processed. This operation regime yields good performance metrics, in terms of mean squared error (MSE) when the signal-to-noise ratio (SNR) is greater than one and shows moderate degradation for SNR values no smaller than -3 dB. Many practical applications require detection for smaller SNR values (weak signals). This chapter presents the theoretical tools and developments that allow nonlinear filtering of weak chaotic signals, avoiding the degradation of the MSE when the SNR is rather small. The innovation introduced through this approach is that the nonlinear filtering becomes multimoment, that is, the influence of more than one moment of time magnitudes is involved in the processing. Some other approaches are also presented.

Suggested Citation

  • Valeri Kontorovich & Zinaida Lovtchikova & Fernando Ramos-Alarcon, 2018. "Nonlinear Filtering of Weak Chaotic Signals," Chapters, in: Kais A. M. Al Naimee (ed.), Chaos Theory, IntechOpen.
    • Handle: RePEc:ito:pchaps:122365
    DOI: 10.5772/intechopen.70717
    as

    Download full text from publisher

    File URL: https://www.intechopen.com/chapters/57108
    Download Restriction: no
    File URL: https://libkey.io/10.5772/intechopen.70717?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
    ---><---

    More about this item

    Keywords

    nonlinear filtering; chaotic systems; Rossler attractor; Lorenz attractor; Chua attractor; Kalman filter; weak signals; mean squared error;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

    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:ito:pchaps:122365. 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: Slobodan Momcilovic (email available below). General contact details of provider: http://www.intechopen.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.