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An algorithm for blind identification of multichannel (single-input and multiple-output) FIR systems is proposed. The proposed algorithm is based on subspace approach to blind identification, which requires so-called noise space spanned by some eigenvectors of correlation matrices of observations. It is shown that a subspace of the noise space can be obtained by one-step scalar-valued linear prediction and then the subspace is sufficient for blind identification. To acquire the subspace, the proposed algorithm utilizes one-step scalar-valued linear prediction in place of a singular- (or eigen-) value decomposition and hence it is computationally efficient. Computer simulations are presented to compare the proposed algorithm with the original one.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.8 pp.1856-1862

- Publication Date
- 2001/08/01

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- Special Section PAPER (Special Section on Digital Signal Processing)

- Category
- Adaptive Signal Processing

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Shuichi OHNO, Hideaki SAKAI, "Blind Identification of Multichannel Systems by Scalar-Valued Linear Prediction" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 8, pp. 1856-1862, August 2001, doi: .

Abstract: An algorithm for blind identification of multichannel (single-input and multiple-output) FIR systems is proposed. The proposed algorithm is based on subspace approach to blind identification, which requires so-called noise space spanned by some eigenvectors of correlation matrices of observations. It is shown that a subspace of the noise space can be obtained by one-step scalar-valued linear prediction and then the subspace is sufficient for blind identification. To acquire the subspace, the proposed algorithm utilizes one-step scalar-valued linear prediction in place of a singular- (or eigen-) value decomposition and hence it is computationally efficient. Computer simulations are presented to compare the proposed algorithm with the original one.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_8_1856/_p

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@ARTICLE{e84-a_8_1856,

author={Shuichi OHNO, Hideaki SAKAI, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={Blind Identification of Multichannel Systems by Scalar-Valued Linear Prediction},

year={2001},

volume={E84-A},

number={8},

pages={1856-1862},

abstract={An algorithm for blind identification of multichannel (single-input and multiple-output) FIR systems is proposed. The proposed algorithm is based on subspace approach to blind identification, which requires so-called noise space spanned by some eigenvectors of correlation matrices of observations. It is shown that a subspace of the noise space can be obtained by one-step scalar-valued linear prediction and then the subspace is sufficient for blind identification. To acquire the subspace, the proposed algorithm utilizes one-step scalar-valued linear prediction in place of a singular- (or eigen-) value decomposition and hence it is computationally efficient. Computer simulations are presented to compare the proposed algorithm with the original one.},

keywords={},

doi={},

ISSN={},

month={August},}

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TY - JOUR

TI - Blind Identification of Multichannel Systems by Scalar-Valued Linear Prediction

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 1856

EP - 1862

AU - Shuichi OHNO

AU - Hideaki SAKAI

PY - 2001

DO -

JO - IEICE TRANSACTIONS on Fundamentals

SN -

VL - E84-A

IS - 8

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - August 2001

AB - An algorithm for blind identification of multichannel (single-input and multiple-output) FIR systems is proposed. The proposed algorithm is based on subspace approach to blind identification, which requires so-called noise space spanned by some eigenvectors of correlation matrices of observations. It is shown that a subspace of the noise space can be obtained by one-step scalar-valued linear prediction and then the subspace is sufficient for blind identification. To acquire the subspace, the proposed algorithm utilizes one-step scalar-valued linear prediction in place of a singular- (or eigen-) value decomposition and hence it is computationally efficient. Computer simulations are presented to compare the proposed algorithm with the original one.

ER -