Computationally Efficient Estimation of Multi-Dimensional Spectral Lines
(2016) IEEE International Conference on Acoustics, Speech and Signal Processing, 2016 In IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)- Abstract
- In this work, we propose a computationally efficient algorithm for estimating multi-dimensional spectral lines. The method treats the data tensor's dimensions separately, yielding the corresponding frequency estimates for each dimension. Then, in a second step, the estimates are ordered over dimensions, thus forming the resulting multidimensional parameter estimates. For high dimensional data, the proposed method offers statistically efficient estimates for moderate to high signal to noise ratios, at a computational cost substantially lower than typical non-parametric Fourier-transform based periodogram solutions, as well as to state-of-the-art parametric estimators.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8515528
- author
- Swärd, Johan
LU
; Adalbjörnsson, Stefan Ingi
LU
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2016-05-19
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Sparse signal modeling., Parameter estimation, Spectral analysis, Efficient algorithms, High-dimensional data
- host publication
- Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on
- series title
- IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE International Conference on Acoustics, Speech and Signal Processing, 2016
- conference location
- Shanghai, China
- conference dates
- 2016-03-20 - 2016-03-25
- external identifiers
-
- scopus:84973359663
- ISSN
- 2379-190X
- ISBN
- 978-1-4799-9988-0
- DOI
- 10.1109/ICASSP.2016.7472606
- language
- English
- LU publication?
- yes
- id
- e1caea61-d155-41b2-b163-78c68c3f3e7d (old id 8515528)
- date added to LUP
- 2016-04-04 11:23:46
- date last changed
- 2022-01-30 05:52:14
@inproceedings{e1caea61-d155-41b2-b163-78c68c3f3e7d, abstract = {{In this work, we propose a computationally efficient algorithm for estimating multi-dimensional spectral lines. The method treats the data tensor's dimensions separately, yielding the corresponding frequency estimates for each dimension. Then, in a second step, the estimates are ordered over dimensions, thus forming the resulting multidimensional parameter estimates. For high dimensional data, the proposed method offers statistically efficient estimates for moderate to high signal to noise ratios, at a computational cost substantially lower than typical non-parametric Fourier-transform based periodogram solutions, as well as to state-of-the-art parametric estimators.}}, author = {{Swärd, Johan and Adalbjörnsson, Stefan Ingi and Jakobsson, Andreas}}, booktitle = {{Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on}}, isbn = {{978-1-4799-9988-0}}, issn = {{2379-190X}}, keywords = {{Sparse signal modeling.; Parameter estimation; Spectral analysis; Efficient algorithms; High-dimensional data}}, language = {{eng}}, month = {{05}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)}}, title = {{Computationally Efficient Estimation of Multi-Dimensional Spectral Lines}}, url = {{https://lup.lub.lu.se/search/files/11875110/8515543.pdf}}, doi = {{10.1109/ICASSP.2016.7472606}}, year = {{2016}}, }