Advanced

Computationally Efficient Estimation of Multi-Dimensional Spectral Lines

Swärd, Johan LU ; Adalbjörnsson, Stefan Ingi LU and Jakobsson, Andreas LU (2016) IEEE International Conference on Acoustics, Speech and Signal Processing, 2016 In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on
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:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Sparse signal modeling., Parameter estimation, Spectral analysis, Efficient algorithms, High-dimensional data
in
Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on
pages
5 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE International Conference on Acoustics, Speech and Signal Processing, 2016
external identifiers
  • Scopus:84973359663
ISSN
2379-190X
DOI
10.1109/ICASSP.2016.7472606
language
English
LU publication?
yes
id
e1caea61-d155-41b2-b163-78c68c3f3e7d (old id 8515528)
date added to LUP
2016-03-21 17:06:06
date last changed
2017-01-01 08:33:26
@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},
  issn         = {2379-190X},
  keyword      = {Sparse signal modeling.,Parameter estimation,Spectral analysis,Efficient algorithms,High-dimensional data},
  language     = {eng},
  month        = {05},
  pages        = {5},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Computationally Efficient Estimation of Multi-Dimensional Spectral Lines},
  url          = {http://dx.doi.org/ 10.1109/ICASSP.2016.7472606},
  year         = {2016},
}