Demixing sparse signals from nonlinear observations

Mohammadreza Soltani, Chinmay Hegde

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Signal demixing is of special importance in several applications ranging from astronomy to computer vision. The goal in demixing is to recover a set of signals from their linear superposition. In this paper, we study the more challenging scenario where only a limited number of nonlinear measurements of the signal superposition are available. Our contribution is a simple, fast algorithm that recovers the component signals from the nonlinear measurements. We support our algorithm with a rigorous theoretical analysis, and provide upper bounds on the estimation error as well as the sample complexity of demixing the components (up to a scalar ambiguity). We also provide a range of simulation results, and observe that the method outperforms a previous algorithm based on convex relaxation.

    Original languageEnglish (US)
    Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
    EditorsMichael B. Matthews
    PublisherIEEE Computer Society
    Number of pages5
    ISBN (Electronic)9781538639542
    StatePublished - Mar 1 2017
    Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
    Duration: Nov 6 2016Nov 9 2016

    Publication series

    NameConference Record - Asilomar Conference on Signals, Systems and Computers
    ISSN (Print)1058-6393


    Other50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
    Country/TerritoryUnited States
    CityPacific Grove

    ASJC Scopus subject areas

    • Signal Processing
    • Computer Networks and Communications


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