ICA of Complex Valued Signals: A Fast and Robust Deflationary Algorithm

Ella Bingham, Aapo Hyvärinen

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    30 Citations (Scopus)


    Separation of complex valued signals is a frequently arising problem in signal processing. In this article it is assumed that the original, complex valued source signals are mutually statistically independent, and the problem is solved by the independent component analysis (ICA) model. ICA is a statistical method for transforming an observed multidimensional random vector into components that are mutually as independent as possible. In this article, a fast fixed-point type algorithm that is capable of separating complex valued, linearly mixed source signals is presented and its computational efficiency is shown by simulations. We also present a theorem on the local consistency of the estimator given by the algorithm.
    Original languageEnglish
    Title of host publicationProceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
    ISBN (Electronic)0-7695-0619-4
    Publication statusPublished - 2000
    MoE publication typeA4 Article in a conference publication
    EventInternational Joint Conference on Neural Networks - Como, Italy
    Duration: 24 Jul 200027 Jul 2000


    ConferenceInternational Joint Conference on Neural Networks
    Abbreviated titleIJCNN


    • complex valued signals
    • deflationary separation
    • independent component analysis


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