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High resolution sparse estimation of exponentially decaying two-dimensional signals

Adalbjörnsson, Stefan Ingi LU ; Swärd, Johan LU and Jakobsson, Andreas LU (2014) 22nd European Signal Processing Conference - EUSIPCO 2014 In European Signal Processing Conference
Abstract
In this work, we consider the problem of high-resolution estimation of the parameters detailing a two-dimensional (2-D) signal consisting of an unknown number of exponentially decaying sinusoidal components. Interpreting the estimation problem as a block (or group) sparse representation problem allows the decoupling of the 2-D data structure into a sum of outer-products of 1-D damped sinusoidal signals with unknown damping and frequency. The resulting non-zero blocks will represent each of the 1-D damped sinusoids, which may then be used as non-parametric estimates of the corresponding 1-D signals; this implies that the sought 2-D modes may be estimated using a sequence of 1-D optimization problems.

The resulting sparse... (More)
In this work, we consider the problem of high-resolution estimation of the parameters detailing a two-dimensional (2-D) signal consisting of an unknown number of exponentially decaying sinusoidal components. Interpreting the estimation problem as a block (or group) sparse representation problem allows the decoupling of the 2-D data structure into a sum of outer-products of 1-D damped sinusoidal signals with unknown damping and frequency. The resulting non-zero blocks will represent each of the 1-D damped sinusoids, which may then be used as non-parametric estimates of the corresponding 1-D signals; this implies that the sought 2-D modes may be estimated using a sequence of 1-D optimization problems.

The resulting sparse representation problem is solved using an iterative ADMM-based algorithm, after which the damping and frequency parameter can be estimated by a sequence of simple 1-D optimization problems. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Sparse signal modeling, Spectral analysis, Sparse reconstruction, Parameter estimation, ADMM.
in
European Signal Processing Conference
pages
5 pages
publisher
EURASIP
conference name
22nd European Signal Processing Conference - EUSIPCO 2014
external identifiers
  • scopus:84911883580
ISSN
2219-5491
language
English
LU publication?
yes
id
3c1af84c-2c9b-4e06-acf1-1b6e0db026c5 (old id 4588725)
alternative location
http://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569925345.pdf
date added to LUP
2014-09-17 11:31:33
date last changed
2017-03-16 09:33:06
@inproceedings{3c1af84c-2c9b-4e06-acf1-1b6e0db026c5,
  abstract     = {In this work, we consider the problem of high-resolution estimation of the parameters detailing a two-dimensional (2-D) signal consisting of an unknown number of exponentially decaying sinusoidal components. Interpreting the estimation problem as a block (or group) sparse representation problem allows the decoupling of the 2-D data structure into a sum of outer-products of 1-D damped sinusoidal signals with unknown damping and frequency. The resulting non-zero blocks will represent each of the 1-D damped sinusoids, which may then be used as non-parametric estimates of the corresponding 1-D signals; this implies that the sought 2-D modes may be estimated using a sequence of 1-D optimization problems. <br/><br>
The resulting sparse representation problem is solved using an iterative ADMM-based algorithm, after which the damping and frequency parameter can be estimated by a sequence of simple 1-D optimization problems.},
  author       = {Adalbjörnsson, Stefan Ingi and Swärd, Johan and Jakobsson, Andreas},
  booktitle    = {European Signal Processing Conference},
  issn         = {2219-5491},
  keyword      = {Sparse signal modeling,Spectral analysis,Sparse reconstruction,Parameter estimation,ADMM.},
  language     = {eng},
  pages        = {5},
  publisher    = {EURASIP},
  title        = {High resolution sparse estimation of exponentially decaying two-dimensional signals},
  year         = {2014},
}