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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 In European Signal Processing Conference
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:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Spectral estimation theory and methods, Sparse Maximum Likelihood methods, fast algorithms
in
European Signal Processing Conference
pages
5 pages
publisher
EURASIP
conference name
22nd European Signal Processing Conference - EUSIPCO 2014
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
2014-10-22 18:05:47
date last changed
2017-03-16 09:33:04
@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},
  keyword      = {Spectral estimation theory and methods,Sparse Maximum Likelihood methods,fast algorithms},
  language     = {eng},
  pages        = {5},
  publisher    = {EURASIP},
  title        = {Efficient Spectral Analysis in the Missing Data Case using Sparse ML Methods},
  year         = {2014},
}