Least Squares and Maximum Likelihood Estimation of Mixed Spectra
(2018) p.2345-2349- Abstract
- In this paper, we propose a novel 1-D spectral
estimator for signals with mixed spectra. The proposed method
is partly based on the recently introduced smooth spectral
estimator LIMES, in which the smoothness is accounted for by
assuming linearity within predefined segments of the spectrum.
The proposed method utilizes this formulation but also allows
segments to change size to better estimate the spectrum, thereby
allowing for the estimation of spectra that are neither completely
smooth or sparse in frequency, but rather contains a mixture
of such components. Using simulated data, we illustrate the
performance of the proposed estimator, comparing to other recent
spectral estimation techniques.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/aed250bf-da22-42d6-9dd1-15b1e87b5086
- author
- Brynolfsson, Johan LU ; Swärd, Johan LU ; Jakobsson, Andreas LU and Sandsten, Maria LU
- organization
- publishing date
- 2018
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 26th European Signal Processing Conference, EUSIPCO 2018.
- article number
- 8553105
- pages
- 2345 - 2349
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85059811194
- ISBN
- 978-908279701-5
- DOI
- 10.23919/EUSIPCO.2018.8553105
- language
- English
- LU publication?
- yes
- id
- aed250bf-da22-42d6-9dd1-15b1e87b5086
- date added to LUP
- 2018-09-26 08:26:55
- date last changed
- 2022-03-25 04:12:32
@inproceedings{aed250bf-da22-42d6-9dd1-15b1e87b5086, abstract = {{In this paper, we propose a novel 1-D spectral<br/>estimator for signals with mixed spectra. The proposed method<br/>is partly based on the recently introduced smooth spectral<br/>estimator LIMES, in which the smoothness is accounted for by<br/>assuming linearity within predefined segments of the spectrum.<br/>The proposed method utilizes this formulation but also allows<br/>segments to change size to better estimate the spectrum, thereby<br/>allowing for the estimation of spectra that are neither completely<br/>smooth or sparse in frequency, but rather contains a mixture<br/>of such components. Using simulated data, we illustrate the<br/>performance of the proposed estimator, comparing to other recent<br/>spectral estimation techniques.}}, author = {{Brynolfsson, Johan and Swärd, Johan and Jakobsson, Andreas and Sandsten, Maria}}, booktitle = {{26th European Signal Processing Conference, EUSIPCO 2018.}}, isbn = {{978-908279701-5}}, language = {{eng}}, pages = {{2345--2349}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Least Squares and Maximum Likelihood Estimation of Mixed Spectra}}, url = {{http://dx.doi.org/10.23919/EUSIPCO.2018.8553105}}, doi = {{10.23919/EUSIPCO.2018.8553105}}, year = {{2018}}, }