Efficient spectral analysis in the missing data case using sparse ML methods. Glentis, G., Karlsson, J., Jakobsson, A., & Li, J. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 1746-1750, Sep., 2014. Paper abstract bibtex 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 further the topic by examining a computationally efficient implementation 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 computational gain as compared to the alternative implementations in the missing data case.
@InProceedings{6952629,
author = {G. Glentis and J. Karlsson and A. Jakobsson and J. Li},
booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
title = {Efficient spectral analysis in the missing data case using sparse ML methods},
year = {2014},
pages = {1746-1750},
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 further the topic by examining a computationally efficient implementation 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 computational gain as compared to the alternative implementations in the missing data case.},
keywords = {maximum likelihood estimation;spectral analysis;missing data case;computational gain;sparse high-resolution spectral estimation;SMLA algorithms;sparse maximum likelihood methods;spectral analysis;Vectors;Zinc;Estimation;Covariance matrices;Tin;Next generation networking;Educational institutions;Spectral estimation theory and methods;Sparse Maximum Likelihood methods;fast algorithms},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569924999.pdf},
}
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