IEICE TRANSACTIONS on Fundamentals
A Unified Framework of Subspace Identification for D.O.A. Estimation
Summary :
In D.O.A. estimation, identification of the signal and the noise subspaces plays an essential role. This identification process was traditionally achieved by the eigenvalue decomposition (EVD) of the spatial correlation matrix of observations or the generalized eigenvalue decomposition (GEVD) of the spatial correlation matrix of observations with respect to that of an observation noise. The framework based on the GEVD is not always an extension of that based on the EVD, since the GEVD is not applicable to the noise-free case which can be resolved by the framework based on the EVD. Moreover, they are not applicable to the case in which the spatial correlation matrix of the noise is singular. Recently, a quotient-singular-value-decomposition-based framework, that can be applied to problems with singular noise correlation matrices, is introduced for noise reduction. However, this framework also can not treat the noise-free case. Thus, we do not have a unified framework of the identification of these subspaces. In this paper, we show that a unified framework of the identification of these subspaces is realized by the concept of proper and improper eigenspaces of the spatial correlation matrix of the noise with respect to that of observations.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E90-A No.2 pp.419-428
- Publication Date
- 2007/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e90-a.2.419
- Type of Manuscript
- PAPER
- Category
- Engineering Acoustics
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Keyword
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Akira TANAKA, Hideyuki IMAI, Masaaki MIYAKOSHI, "A Unified Framework of Subspace Identification for D.O.A. Estimation" in IEICE TRANSACTIONS on Fundamentals,
vol. E90-A, no. 2, pp. 419-428, February 2007, doi: 10.1093/ietfec/e90-a.2.419.
Abstract: In D.O.A. estimation, identification of the signal and the noise subspaces plays an essential role. This identification process was traditionally achieved by the eigenvalue decomposition (EVD) of the spatial correlation matrix of observations or the generalized eigenvalue decomposition (GEVD) of the spatial correlation matrix of observations with respect to that of an observation noise. The framework based on the GEVD is not always an extension of that based on the EVD, since the GEVD is not applicable to the noise-free case which can be resolved by the framework based on the EVD. Moreover, they are not applicable to the case in which the spatial correlation matrix of the noise is singular. Recently, a quotient-singular-value-decomposition-based framework, that can be applied to problems with singular noise correlation matrices, is introduced for noise reduction. However, this framework also can not treat the noise-free case. Thus, we do not have a unified framework of the identification of these subspaces. In this paper, we show that a unified framework of the identification of these subspaces is realized by the concept of proper and improper eigenspaces of the spatial correlation matrix of the noise with respect to that of observations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.2.419/_p
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@ARTICLE{e90-a_2_419,
author={Akira TANAKA, Hideyuki IMAI, Masaaki MIYAKOSHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Unified Framework of Subspace Identification for D.O.A. Estimation},
year={2007},
volume={E90-A},
number={2},
pages={419-428},
abstract={In D.O.A. estimation, identification of the signal and the noise subspaces plays an essential role. This identification process was traditionally achieved by the eigenvalue decomposition (EVD) of the spatial correlation matrix of observations or the generalized eigenvalue decomposition (GEVD) of the spatial correlation matrix of observations with respect to that of an observation noise. The framework based on the GEVD is not always an extension of that based on the EVD, since the GEVD is not applicable to the noise-free case which can be resolved by the framework based on the EVD. Moreover, they are not applicable to the case in which the spatial correlation matrix of the noise is singular. Recently, a quotient-singular-value-decomposition-based framework, that can be applied to problems with singular noise correlation matrices, is introduced for noise reduction. However, this framework also can not treat the noise-free case. Thus, we do not have a unified framework of the identification of these subspaces. In this paper, we show that a unified framework of the identification of these subspaces is realized by the concept of proper and improper eigenspaces of the spatial correlation matrix of the noise with respect to that of observations.},
keywords={},
doi={10.1093/ietfec/e90-a.2.419},
ISSN={1745-1337},
month={February},}
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TY - JOUR
TI - A Unified Framework of Subspace Identification for D.O.A. Estimation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 419
EP - 428
AU - Akira TANAKA
AU - Hideyuki IMAI
AU - Masaaki MIYAKOSHI
PY - 2007
DO - 10.1093/ietfec/e90-a.2.419
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2007
AB - In D.O.A. estimation, identification of the signal and the noise subspaces plays an essential role. This identification process was traditionally achieved by the eigenvalue decomposition (EVD) of the spatial correlation matrix of observations or the generalized eigenvalue decomposition (GEVD) of the spatial correlation matrix of observations with respect to that of an observation noise. The framework based on the GEVD is not always an extension of that based on the EVD, since the GEVD is not applicable to the noise-free case which can be resolved by the framework based on the EVD. Moreover, they are not applicable to the case in which the spatial correlation matrix of the noise is singular. Recently, a quotient-singular-value-decomposition-based framework, that can be applied to problems with singular noise correlation matrices, is introduced for noise reduction. However, this framework also can not treat the noise-free case. Thus, we do not have a unified framework of the identification of these subspaces. In this paper, we show that a unified framework of the identification of these subspaces is realized by the concept of proper and improper eigenspaces of the spatial correlation matrix of the noise with respect to that of observations.
ER -
English
