Multi-dimensional grid-less estimation of saturated signals

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This work proposes a multi-dimensional 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
Pages (from-to)37-47
Number of pages11
JournalSignal Processing
Publication statusPublished - 2018

Subject classification (UKÄ)

  • Signal Processing
  • Probability Theory and Statistics


  • Atomic norm
  • De-clipping
  • Gridless reconstruction

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