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.
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.
| Original language | English |
|---|---|
| Title of host publication | European Signal Processing Conference |
| Publisher | EURASIP |
| Number of pages | 5 |
| Publication status | Published - 2014 |
| Event | 22nd European Signal Processing Conference - EUSIPCO 2014 - Lissabon, Portugal Duration: 2014 Sept 1 → 2014 Sept 5 Conference number: 22 |
Publication series
| Name | |
|---|---|
| ISSN (Print) | 2219-5491 |
Conference
| Conference | 22nd European Signal Processing Conference - EUSIPCO 2014 |
|---|---|
| Country/Territory | Portugal |
| City | Lissabon |
| Period | 2014/09/01 → 2014/09/05 |
Subject classification (UKÄ)
- Probability Theory and Statistics
Free keywords
- Spectral estimation theory and methods
- Sparse Maximum Likelihood methods
- fast algorithms