Grid-less estimation of saturated signals

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 wa...

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Bibliographic Details
Main Authors: Elvander, Filip, Sward, Johan, Jakobsson, Andreas
Format: Conference Proceeding
Language:English
Subjects:
atomic norm
de-clipping
Frequency estimation
gridless reconstruction
Image reconstruction
Minimization
Noise measurement
Optimization
Robustness
Signal to noise ratio
Online Access:Request full text
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Description
Summary: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.
ISSN:2576-2303
DOI:10.1109/ACSSC.2017.8335204