• DocumentCode
    3116262
  • Title

    Online Detection of the Nature of Complex-Valued Signals

  • Author

    Vayanos, Phebe ; Goh, Su Lee ; Mandic, Danilo P.

  • Author_Institution
    Imperial Coll. London, London
  • fYear
    2006
  • fDate
    6-8 Sept. 2006
  • Firstpage
    173
  • Lastpage
    178
  • Abstract
    A novel method for on-line tracking of the changes in the nature of a complex-valued signal is proposed. This is achieved by analysing the time variation of the mixing parameter within a hybrid complex-valued nonlinear adaptive filter. The proposed hybrid filter consists of a combination of split- and fully-complex nonlinear gradient descent algorithms, whose outputs are mixed in a convex manner. A learning algorithm for this scheme is derived and the potential of such an approach for tracking of signal modality changes is highlighted. The potential of the proposed approach is supported by simulations on both a synthetic benchmark signal and on real-world radar data.
  • Keywords
    adaptive filters; filtering theory; gradient methods; nonlinear filters; signal detection; complex-valued nonlinear adaptive filter; complex-valued signal detection; fully-complex nonlinear gradient descent algorithm; learning algorithm; split-complex nonlinear gradient descent algorithm; time variation analysis; Adaptive filters; Backpropagation algorithms; Educational institutions; Machine learning algorithms; Neural networks; Neurons; Radar tracking; Signal processing; Signal processing algorithms; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on
  • Conference_Location
    Arlington, VA
  • ISSN
    1551-2541
  • Print_ISBN
    1-4244-0656-0
  • Electronic_ISBN
    1551-2541
  • Type

    conf

  • DOI
    10.1109/MLSP.2006.275543
  • Filename
    4053642