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
- 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 (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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/84bd366d-70ca-4fa8-9ead-8dd866247de8
- author
- Liu, Kefei
; Cao, Hui
; So, Hing Cheung
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2017-02-09
- 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
- 2022-02-14 17:00:31
@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}},
doi = {{10.1016/j.dsp.2017.01.012}},
volume = {{64}},
year = {{2017}},
}