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Multi-dimensional sinusoidal order estimation using angles between subspaces

Liu, Kefei; Cao, Hui; So, Hing Cheung and Jakobsson, Andreas LU (2017) In Digital Signal Processing 64. p.17-27
Abstract (Swedish)
Multi-dimensional harmonic retrieval (HR) in white noise is required in numerous applications such as channel estimation in wireless communications and imaging in multiple-input multiple-output radar. In this paper, we propose two R-dimensional (R -D) extensions of the subspace-based MUSIC model order selection scheme, for R≥2R≥2, to detect the number of multi-dimensional cisoids. The key idea in the algorithm development is to utilize the principle angles between multilinear signal subspaces via the truncated higher-order singular value decomposition. The first method is designed for multiple-snapshot scenarios. It considerably outperforms existing algorithms in terms of both detection accuracy and identifiability particularly when a... (More)
Multi-dimensional harmonic retrieval (HR) in white noise is required in numerous applications such as channel estimation in wireless communications and imaging in multiple-input multiple-output radar. In this paper, we propose two R-dimensional (R -D) extensions of the subspace-based MUSIC model order selection scheme, for R≥2R≥2, to detect the number of multi-dimensional cisoids. The key idea in the algorithm development is to utilize the principle angles between multilinear signal subspaces via the truncated higher-order singular value decomposition. The first method is designed for multiple-snapshot scenarios. It considerably outperforms existing algorithms in terms of both detection accuracy and identifiability particularly when a large number of snapshots are available. However, its computational cost is relatively quite high. The second method is computationally much simpler and performs almost as well as the first one when the number of snapshots is small. Simulation results are conducted to demonstrate the performance of the proposed estimators. (Less)
Abstract
Multi-dimensional harmonic retrieval (HR) in white noise is required in numerous applications such as channel estimation in wireless communications and imaging in multiple-input multiple-output radar. In this paper, we propose two R-dimensional (R -D) extensions of the subspace-based MUSIC model order selection scheme, for R≥2R≥2, to detect the number of multi-dimensional cisoids. The key idea in the algorithm development is to utilize the principle angles between multilinear signal subspaces via the truncated higher-order singular value decomposition. The first method is designed for multiple-snapshot scenarios. It considerably outperforms existing algorithms in terms of both detection accuracy and identifiability particularly when a... (More)
Multi-dimensional harmonic retrieval (HR) in white noise is required in numerous applications such as channel estimation in wireless communications and imaging in multiple-input multiple-output radar. In this paper, we propose two R-dimensional (R -D) extensions of the subspace-based MUSIC model order selection scheme, for R≥2R≥2, to detect the number of multi-dimensional cisoids. The key idea in the algorithm development is to utilize the principle angles between multilinear signal subspaces via the truncated higher-order singular value decomposition. The first method is designed for multiple-snapshot scenarios. It considerably outperforms existing algorithms in terms of both detection accuracy and identifiability particularly when a large number of snapshots are available. However, its computational cost is relatively quite high. The second method is computationally much simpler and performs almost as well as the first one when the number of snapshots is small. Simulation results are conducted to demonstrate the performance of the proposed estimators. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Digital Signal Processing
volume
64
pages
17 - 27
publisher
Elsevier
external identifiers
  • scopus:85012981807
  • wos:000399257000003
ISSN
1095-4333
DOI
10.1016/j.dsp.2017.01.012
language
English
LU publication?
yes
id
84bd366d-70ca-4fa8-9ead-8dd866247de8
date added to LUP
2017-02-14 10:21:45
date last changed
2018-01-07 11:49:36
@article{84bd366d-70ca-4fa8-9ead-8dd866247de8,
  abstract     = {Multi-dimensional harmonic retrieval (HR) in white noise is required in numerous applications such as channel estimation in wireless communications and imaging in multiple-input multiple-output radar. In this paper, we propose two R-dimensional (R  -D) extensions of the subspace-based MUSIC model order selection scheme, for R≥2R≥2, to detect the number of multi-dimensional cisoids. The key idea in the algorithm development is to utilize the principle angles between multilinear signal subspaces via the truncated higher-order singular value decomposition. The first method is designed for multiple-snapshot scenarios. It considerably outperforms existing algorithms in terms of both detection accuracy and identifiability particularly when a large number of snapshots are available. However, its computational cost is relatively quite high. The second method is computationally much simpler and performs almost as well as the first one when the number of snapshots is small. Simulation results are conducted to demonstrate the performance of the proposed estimators.},
  author       = {Liu, Kefei and Cao, Hui and So, Hing Cheung and Jakobsson, Andreas},
  issn         = {1095-4333},
  language     = {eng},
  month        = {02},
  pages        = {17--27},
  publisher    = {Elsevier},
  series       = {Digital Signal Processing},
  title        = {Multi-dimensional sinusoidal order estimation using angles between subspaces},
  url          = {http://dx.doi.org/10.1016/j.dsp.2017.01.012},
  volume       = {64},
  year         = {2017},
}