A fast iterative algorithm for demixing sparse signals from nonlinear observations

Mohammadreza Soltani, Chinmay Hegde

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

    Abstract

    In this paper, we propose an iterative algorithm based on hard thresholding for demixing a pair of signals from nonlinear observations of their superposition. We focus on the under-determined case where the number of available observations is far less than the ambient dimension of the signals. We derive nearly-tight upper bounds on the sample complexity of the algorithm to achieve stable recovery of the component signals. Moreover, we show that the algorithm enjoys a linear convergence rate. We provide a range of simulations to illustrate the performance of the algorithm both on synthetic and real data.

    Original languageEnglish (US)
    Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages167-171
    Number of pages5
    ISBN (Electronic)9781509045457
    DOIs
    StatePublished - Apr 19 2017
    Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
    Duration: Dec 7 2016Dec 9 2016

    Publication series

    Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

    Other

    Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
    CountryUnited States
    CityWashington
    Period12/7/1612/9/16

    Keywords

    • Demixing
    • Incoherence
    • Linear convergence
    • Nonlinear measurements
    • Sparse recovery

    ASJC Scopus subject areas

    • Signal Processing
    • Computer Networks and Communications

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