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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 (Swedish)
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... (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:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Eurasip Journal on Advances in Signal Processing
publisher
Hindawi Publishing Corporation
external identifiers
  • wos:000273223000001
  • scopus:73449100765
ISSN
1687-6172
DOI
10.1155/2009/948756
language
English
LU publication?
yes
id
2cae0d08-ccdd-4ade-9cfc-c222659c9cab (old id 1471431)
date added to LUP
2009-09-17 13:34:45
date last changed
2017-08-27 04:44:45
@article{2cae0d08-ccdd-4ade-9cfc-c222659c9cab,
  abstract     = {<b>Abstract in Undetermined</b><br/><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.},
  articleno    = {948756},
  author       = {Christensen, Mads and Jakobsson, Andreas and Jensen, Sören},
  issn         = {1687-6172},
  language     = {eng},
  publisher    = {Hindawi Publishing Corporation},
  series       = {Eurasip Journal on Advances in Signal Processing},
  title        = {Sinusoidal Order Estimation using Angles between Subspaces},
  url          = {http://dx.doi.org/10.1155/2009/948756},
  year         = {2009},
}