Grid-less estimation of saturated signals

Filip Elvander; Johan Swärd; Andreas Jakobsson

doi:10.1109/ACSSC.2017.8335204

Grid-less estimation of saturated signals

Filip Elvander, Johan Swärd, Andreas Jakobsson

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

Sammanfattning

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.

Originalspråk	engelska
Titel på värdpublikation	2017 51st Asilomar Conference on Signals, Systems, and Computers
Förlag	IEEE - Institute of Electrical and Electronics Engineers Inc.
Sidor	372-376
Antal sidor	5
ISBN (elektroniskt)	978-1-5386-1823-3
DOI	https://doi.org/10.1109/ACSSC.2017.8335204
Status	Published - 2017
Evenemang	51st Asilomar Conferenec on Signals, Systems, and Computers (ASILOMAR 2017) - Asilomar, Pacific Grove, USA Varaktighet: 2017 okt. 29 → 2017 nov. 1

Konferens

Konferens	51st Asilomar Conferenec on Signals, Systems, and Computers (ASILOMAR 2017)
Land/Territorium	USA
Ort	Pacific Grove
Period	2017/10/29 → 2017/11/01

Ämnesklassifikation (UKÄ)

Signalbehandling

Tillgång till dokument

10.1109/ACSSC.2017.8335204

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@inproceedings{430443542e744f0a9450e95c56dc20f4,

title = "Grid-less estimation of saturated signals",

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.",

keywords = "atomic norm, de-clipping, gridless reconstruction",

author = "Filip Elvander and Johan Sw{\"a}rd and Andreas Jakobsson",

year = "2017",

doi = "10.1109/ACSSC.2017.8335204",

language = "English",

pages = "372--376",

booktitle = "2017 51st Asilomar Conference on Signals, Systems, and Computers",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "51st Asilomar Conferenec on Signals, Systems, and Computers (ASILOMAR 2017) ; Conference date: 29-10-2017 Through 01-11-2017",

}

Elvander, F , Swärd, J & Jakobsson, A 2017, Grid-less estimation of saturated signals. i 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE - Institute of Electrical and Electronics Engineers Inc., s. 372-376, 51st Asilomar Conferenec on Signals, Systems, and Computers (ASILOMAR 2017), Pacific Grove, Kalifornien, USA, 2017/10/29. https://doi.org/10.1109/ACSSC.2017.8335204

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AU - Elvander, Filip

AU - Swärd, Johan

AU - Jakobsson, Andreas

PY - 2017

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N2 - 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.

AB - 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.

KW - atomic norm

KW - de-clipping

KW - gridless reconstruction

U2 - 10.1109/ACSSC.2017.8335204

DO - 10.1109/ACSSC.2017.8335204

M3 - Paper in conference proceeding

SP - 372

EP - 376

BT - 2017 51st Asilomar Conference on Signals, Systems, and Computers

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 51st Asilomar Conferenec on Signals, Systems, and Computers (ASILOMAR 2017)

Y2 - 29 October 2017 through 1 November 2017

ER -