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

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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 language	English
Title of host publication	2017 51st Asilomar Conference on Signals, Systems, and Computers
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	372-376
Number of pages	5
ISBN (Electronic)	978-1-5386-1823-3
DOIs	https://doi.org/10.1109/ACSSC.2017.8335204
Publication status	Published - 2017
Event	51st Asilomar Conferenec on Signals, Systems, and Computers (ASILOMAR 2017) - Asilomar, Pacific Grove, United States Duration: 2017 Oct 29 → 2017 Nov 1

Conference

Conference	51st Asilomar Conferenec on Signals, Systems, and Computers (ASILOMAR 2017)
Country/Territory	United States
City	Pacific Grove
Period	2017/10/29 → 2017/11/01

Subject classification (UKÄ)

Signal Processing

Keywords

atomic norm
de-clipping
gridless reconstruction

Access to Document

10.1109/ACSSC.2017.8335204

Cite this

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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. in 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 372-376, 51st Asilomar Conferenec on Signals, Systems, and Computers (ASILOMAR 2017), Pacific Grove, California, United States, 2017/10/29. https://doi.org/10.1109/ACSSC.2017.8335204

TY - GEN

T1 - Grid-less estimation of saturated signals

AU - Elvander, Filip

AU - Swärd, Johan

AU - Jakobsson, Andreas

PY - 2017

Y1 - 2017

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 -