Grid-less estimation of saturated signals

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Abstract

This work proposes a frequency and amplitude estimator tailored for noise corrupted signals that have been clipped. Formulated as a sparse reconstruction problem, the proposed algorithm estimates the signal parameters by solving an atomic norm minimization problem. The estimator also exploits the waveform information provided by the clipped samples, incorporated in the form of linear constraints that have been augmented by slack variables as to provide robustness to noise. Numerical examples indicate that the algorithm offers preferable performance as compared to methods not exploiting the saturated samples.
Original languageEnglish
Title of host publication2017 51st Asilomar Conference on Signals, Systems, and Computers
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages372-376
Number of pages5
ISBN (Electronic)978-1-5386-1823-3
DOIs
Publication statusPublished - 2017
Event51st Asilomar Conferenec on Signals, Systems, and Computers (ASILOMAR 2017) - Asilomar, Pacific Grove, United States
Duration: 2017 Oct 292017 Nov 1

Conference

Conference51st Asilomar Conferenec on Signals, Systems, and Computers (ASILOMAR 2017)
Country/TerritoryUnited States
CityPacific Grove
Period2017/10/292017/11/01

Subject classification (UKÄ)

  • Signal Processing

Keywords

  • atomic norm
  • de-clipping
  • gridless reconstruction

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