Computationally Efficient Estimation of Multi-Dimensional Spectral Lines

Johan Swärd; Stefan Ingi Adalbjörnsson; Andreas Jakobsson

doi:10.1109/ICASSP.2016.7472606

Computationally Efficient Estimation of Multi-Dimensional Spectral Lines

Johan Swärd, Stefan Ingi Adalbjörnsson, Andreas Jakobsson

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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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.

Original language	English
Title of host publication	Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Number of pages	5
ISBN (Electronic)	978-1-4799-9988-0
DOIs	https://doi.org/10.1109/ICASSP.2016.7472606
Publication status	Published - 2016 May 19
Event	IEEE International Conference on Acoustics, Speech and Signal Processing, 2016 - Shanghai, China Duration: 2016 Mar 20 → 2016 Mar 25 Conference number: 41

Publication series

Name	IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)
Publisher	IEEE
ISSN (Electronic)	2379-190X

Conference

Conference	IEEE International Conference on Acoustics, Speech and Signal Processing, 2016
Abbreviated title	ICASSP 2016
Country/Territory	China
City	Shanghai
Period	2016/03/20 → 2016/03/25

Subject classification (UKÄ)

Probability Theory and Statistics
Signal Processing

Free keywords

Sparse signal modeling.
Parameter estimation
Spectral analysis
Efficient algorithms
High-dimensional data

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10.1109/ICASSP.2016.7472606

SwardAJ16_ICASSP

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Swärd, J., Adalbjörnsson, S. I., & Jakobsson, A. (2016). Computationally Efficient Estimation of Multi-Dimensional Spectral Lines. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on (IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2016.7472606

Swärd, Johan ; Adalbjörnsson, Stefan Ingi ; Jakobsson, Andreas. / Computationally Efficient Estimation of Multi-Dimensional Spectral Lines. Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE - Institute of Electrical and Electronics Engineers Inc., 2016. (IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)).

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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.",

keywords = "Sparse signal modeling., Parameter estimation, Spectral analysis, Efficient algorithms, High-dimensional data",

author = "Johan Sw{\"a}rd and Adalbj{\"o}rnsson, {Stefan Ingi} and Andreas Jakobsson",

year = "2016",

month = may,

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doi = "10.1109/ICASSP.2016.7472606",

language = "English",

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booktitle = "Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on",

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note = "IEEE International Conference on Acoustics, Speech and Signal Processing, 2016, ICASSP 2016 ; Conference date: 20-03-2016 Through 25-03-2016",

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Swärd, J, Adalbjörnsson, SI & Jakobsson, A 2016, Computationally Efficient Estimation of Multi-Dimensional Spectral Lines. in Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), IEEE - Institute of Electrical and Electronics Engineers Inc., IEEE International Conference on Acoustics, Speech and Signal Processing, 2016, Shanghai, China, 2016/03/20. https://doi.org/10.1109/ICASSP.2016.7472606

Computationally Efficient Estimation of Multi-Dimensional Spectral Lines. / Swärd, Johan; Adalbjörnsson, Stefan Ingi; Jakobsson, Andreas.
Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE - Institute of Electrical and Electronics Engineers Inc., 2016. (IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)).

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Computationally Efficient Estimation of Multi-Dimensional Spectral Lines

AU - Swärd, Johan

AU - Adalbjörnsson, Stefan Ingi

AU - Jakobsson, Andreas

N1 - Conference code: 41

PY - 2016/5/19

Y1 - 2016/5/19

N2 - 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.

AB - 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.

KW - Sparse signal modeling.

KW - Parameter estimation

KW - Spectral analysis

KW - Efficient algorithms

KW - High-dimensional data

U2 - 10.1109/ICASSP.2016.7472606

DO - 10.1109/ICASSP.2016.7472606

M3 - Paper in conference proceeding

T3 - IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)

BT - Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE International Conference on Acoustics, Speech and Signal Processing, 2016

Y2 - 20 March 2016 through 25 March 2016

ER -

Swärd J, Adalbjörnsson SI, Jakobsson A. Computationally Efficient Estimation of Multi-Dimensional Spectral Lines. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE - Institute of Electrical and Electronics Engineers Inc. 2016. (IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)). doi: 10.1109/ICASSP.2016.7472606

Computationally Efficient Estimation of Multi-Dimensional Spectral Lines

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