Sinusoidal Order Estimation using Angles between Subspaces
Christensen, Mads ; Jakobsson, Andreas LU
and Jensen, Sören
(2009)
In Eurasip Journal on Advances in Signal Processing
- Abstract
- Abstract in Undetermined
We consider the problem of determining the order of a parametric model from a noisy signal based on the geometry of the space. More specifically, we do this using the nontrivial angles between the candidate signal subspace model and the noise subspace. The proposed principle is closely related to the subspace orthogonality property known from the MUSIC algorithm, and we study its properties and compare it to other related measures. For the problem of estimating the number of complex sinusoids in white noise, a computationally efficient implementation exists, and this problem is therefore considered in detail. In computer simulations, we compare the proposed method to various well-known methods for order... (More) - Abstract in Undetermined
We consider the problem of determining the order of a parametric model from a noisy signal based on the geometry of the space. More specifically, we do this using the nontrivial angles between the candidate signal subspace model and the noise subspace. The proposed principle is closely related to the subspace orthogonality property known from the MUSIC algorithm, and we study its properties and compare it to other related measures. For the problem of estimating the number of complex sinusoids in white noise, a computationally efficient implementation exists, and this problem is therefore considered in detail. In computer simulations, we compare the proposed method to various well-known methods for order estimation. These show that the proposed method outperforms the other previously published subspace methods and that it is more robust to the noise being colored than the previously published methods. Copyright (C) 2009 Mads Graesboll Christensen et al. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1471431
- author
- Christensen, Mads
; Jakobsson, Andreas
LU
and Jensen, Sören
- organization
- publishing date
- 2009
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Eurasip Journal on Advances in Signal Processing
- article number
- 948756
- publisher
- Hindawi Limited
- external identifiers
-
- wos:000273223000001
- scopus:73449100765
- ISSN
- 1687-6172
- DOI
- 10.1155/2009/948756
- language
- English
- LU publication?
- yes
- additional info
- provisional pdf: http://www.hindawi.com/journals/asp/aip.948756.pdf
- id
- 2cae0d08-ccdd-4ade-9cfc-c222659c9cab (old id 1471431)
- date added to LUP
- 2016-04-01 13:54:16
- date last changed
- 2022-04-22 00:16:18
@article{2cae0d08-ccdd-4ade-9cfc-c222659c9cab,
abstract = {{Abstract in Undetermined<br/>We consider the problem of determining the order of a parametric model from a noisy signal based on the geometry of the space. More specifically, we do this using the nontrivial angles between the candidate signal subspace model and the noise subspace. The proposed principle is closely related to the subspace orthogonality property known from the MUSIC algorithm, and we study its properties and compare it to other related measures. For the problem of estimating the number of complex sinusoids in white noise, a computationally efficient implementation exists, and this problem is therefore considered in detail. In computer simulations, we compare the proposed method to various well-known methods for order estimation. These show that the proposed method outperforms the other previously published subspace methods and that it is more robust to the noise being colored than the previously published methods. Copyright (C) 2009 Mads Graesboll Christensen et al.}},
author = {{Christensen, Mads and Jakobsson, Andreas and Jensen, Sören}},
issn = {{1687-6172}},
language = {{eng}},
publisher = {{Hindawi Limited}},
series = {{Eurasip Journal on Advances in Signal Processing}},
title = {{Sinusoidal Order Estimation using Angles between Subspaces}},
url = {{https://lup.lub.lu.se/search/files/3658988/3993865.pdf}},
doi = {{10.1155/2009/948756}},
year = {{2009}},
}