Efficient Spectral Analysis in the Missing Data Case using Sparse ML Methods
Glentis, George ; Karlsson, Johan ; Jakobsson, Andreas LU
and Li, Jian
(2014)
22nd European Signal Processing Conference - EUSIPCO 2014
- Abstract
- Given their wide applicability, several sparse high-resolution
spectral estimation techniques and their implementation have
been examined in the recent literature. In this work, we fur-
ther the topic by examining a computationally efficient im-
plementation of the recent SMLA algorithms in the missing
data case. The work is an extension of our implementation
for the uniformly sampled case, and offers a notable compu-
tational gain as compared to the alternative implementations
in the missing data case.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4645572
- author
- Glentis, George
; Karlsson, Johan
; Jakobsson, Andreas
LU
and Li, Jian
- organization
- publishing date
- 2014
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Spectral estimation theory and methods, Sparse Maximum Likelihood methods, fast algorithms
- host publication
- European Signal Processing Conference
- pages
- 5 pages
- publisher
- EURASIP
- conference name
- 22nd European Signal Processing Conference - EUSIPCO 2014
- conference location
- Lissabon, Portugal
- conference dates
- 2014-09-01 - 2014-09-05
- external identifiers
-
- scopus:84911895171
- ISSN
- 2219-5491
- language
- English
- LU publication?
- yes
- id
- 17007e38-342a-4a54-9777-ca8a3ee22cf5 (old id 4645572)
- alternative location
- http://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569924999.pdf
- date added to LUP
- 2016-04-01 13:03:05
- date last changed
- 2022-01-27 17:00:56
@inproceedings{17007e38-342a-4a54-9777-ca8a3ee22cf5,
abstract = {{Given their wide applicability, several sparse high-resolution<br/><br>
spectral estimation techniques and their implementation have<br/><br>
been examined in the recent literature. In this work, we fur-<br/><br>
ther the topic by examining a computationally efficient im-<br/><br>
plementation of the recent SMLA algorithms in the missing<br/><br>
data case. The work is an extension of our implementation<br/><br>
for the uniformly sampled case, and offers a notable compu-<br/><br>
tational gain as compared to the alternative implementations<br/><br>
in the missing data case.}},
author = {{Glentis, George and Karlsson, Johan and Jakobsson, Andreas and Li, Jian}},
booktitle = {{European Signal Processing Conference}},
issn = {{2219-5491}},
keywords = {{Spectral estimation theory and methods; Sparse Maximum Likelihood methods; fast algorithms}},
language = {{eng}},
publisher = {{EURASIP}},
title = {{Efficient Spectral Analysis in the Missing Data Case using Sparse ML Methods}},
url = {{http://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569924999.pdf}},
year = {{2014}},
}