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High resolution sparse estimation of exponentially decaying signals

Swärd, Johan LU ; Adalbjörnsson, Stefan Ingi LU and Jakobsson, Andreas LU (2014) 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on p.7203-7207
Abstract
We consider the problem of sparse modeling of a signal consisting of an unknown number of exponentially decaying sinusoids. Since such signals are not sparse in an oversampled Fourier matrix, earlier approaches typically exploit large dictionary matrices that include not only a finely spaced frequency grid but also a grid over the considered damping factors. The resulting dictionary is often very large, resulting in a computationally cumbersome optimization problem. Here, we instead introduce a novel dictionary learning approach that iteratively refines the estimate of the candidate damping factor for each sinusoid, thus allowing for both a quite small dictionary and for arbitrary damping factors, not being restricted to a grid. The... (More)
We consider the problem of sparse modeling of a signal consisting of an unknown number of exponentially decaying sinusoids. Since such signals are not sparse in an oversampled Fourier matrix, earlier approaches typically exploit large dictionary matrices that include not only a finely spaced frequency grid but also a grid over the considered damping factors. The resulting dictionary is often very large, resulting in a computationally cumbersome optimization problem. Here, we instead introduce a novel dictionary learning approach that iteratively refines the estimate of the candidate damping factor for each sinusoid, thus allowing for both a quite small dictionary and for arbitrary damping factors, not being restricted to a grid. The performance of the proposed method is illustrated using simulated data, clearly showing the improved performance as compared to previous techniques. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Parameter estimation, Sparse reconstruction, Sparse signal modeling, Spectral analysis
in
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
pages
5 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)
external identifiers
  • wos:000343655307048
  • scopus:84905233550
ISSN
1520-6149
DOI
10.1109/ICASSP.2014.6854998
language
English
LU publication?
yes
id
94a5d797-f0f4-4569-a563-75042cfe7025 (old id 4588655)
date added to LUP
2014-08-29 20:45:38
date last changed
2017-04-16 03:42:37
@inproceedings{94a5d797-f0f4-4569-a563-75042cfe7025,
  abstract     = {We consider the problem of sparse modeling of a signal consisting of an unknown number of exponentially decaying sinusoids. Since such signals are not sparse in an oversampled Fourier matrix, earlier approaches typically exploit large dictionary matrices that include not only a finely spaced frequency grid but also a grid over the considered damping factors. The resulting dictionary is often very large, resulting in a computationally cumbersome optimization problem. Here, we instead introduce a novel dictionary learning approach that iteratively refines the estimate of the candidate damping factor for each sinusoid, thus allowing for both a quite small dictionary and for arbitrary damping factors, not being restricted to a grid. The performance of the proposed method is illustrated using simulated data, clearly showing the improved performance as compared to previous techniques.},
  author       = {Swärd, Johan and Adalbjörnsson, Stefan Ingi and Jakobsson, Andreas},
  booktitle    = {Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on},
  issn         = {1520-6149},
  keyword      = {Parameter estimation,Sparse reconstruction,Sparse signal modeling,Spectral analysis},
  language     = {eng},
  pages        = {7203--7207},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {High resolution sparse estimation of exponentially decaying signals},
  url          = {http://dx.doi.org/10.1109/ICASSP.2014.6854998},
  year         = {2014},
}