High resolution sparse estimation of exponentially decaying signals
(2014) 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014) 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:
https://lup.lub.lu.se/record/4588655
- author
- Swärd, Johan LU ; Adalbjörnsson, Stefan Ingi LU and Jakobsson, Andreas LU
- organization
- publishing date
- 2014
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Parameter estimation, Sparse reconstruction, Sparse signal modeling, Spectral analysis
- host publication
- 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)
- conference location
- Florence, Italy
- conference dates
- 2014-05-04 - 2014-05-09
- 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
- 2016-04-01 12:50:51
- date last changed
- 2022-03-13 20:43:44
@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}}, keywords = {{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 = {{https://lup.lub.lu.se/search/files/3009171/4588701.pdf}}, doi = {{10.1109/ICASSP.2014.6854998}}, year = {{2014}}, }