Multi-dimensional grid-less estimation of saturated signals
Elvander, Filip LU ; Swärd, Johan LU and Jakobsson, Andreas LU
(2018)
In Signal Processing
145.
p.37-47
- Abstract
- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/b568dbea-aba1-4110-bbfa-16950fece073
- author
- Elvander, Filip
LU
; Swärd, Johan
LU
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2018
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Atomic norm, De-clipping, Gridless reconstruction
- in
- Signal Processing
- volume
- 145
- pages
- 11 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85034743727
- ISSN
- 0165-1684
- DOI
- 10.1016/j.sigpro.2017.11.008
- language
- English
- LU publication?
- yes
- id
- b568dbea-aba1-4110-bbfa-16950fece073
- date added to LUP
- 2017-12-03 17:04:28
- date last changed
- 2022-03-24 22:42:15
@article{b568dbea-aba1-4110-bbfa-16950fece073,
abstract = {{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.}},
author = {{Elvander, Filip and Swärd, Johan and Jakobsson, Andreas}},
issn = {{0165-1684}},
keywords = {{Atomic norm; De-clipping; Gridless reconstruction}},
language = {{eng}},
pages = {{37--47}},
publisher = {{Elsevier}},
series = {{Signal Processing}},
title = {{Multi-dimensional grid-less estimation of saturated signals}},
url = {{http://dx.doi.org/10.1016/j.sigpro.2017.11.008}},
doi = {{10.1016/j.sigpro.2017.11.008}},
volume = {{145}},
year = {{2018}},
}