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This paper focuses on fusion estimation algorithms weighted by matrices and scalars, and relationship between them is considered. We present new algorithms that address the computation of matrix weights arising from multidimensional estimation problems. The first algorithm is based on the Cholesky factorization of a cross-covariance block-matrix. This algorithm is equivalent to the standard composite fusion estimation algorithm however it is low-complexity. The second fusion algorithm is based on an approximation scheme which uses special steady-state approximation for local cross-covariances. Such approximation is useful for computing matrix weights in real-time. Subsequent analysis of the proposed fusion algorithms is presented, in which examples demonstrate the low-computational complexity of the new fusion estimation algorithms.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.11 pp.2910-2916

- Publication Date
- 2009/11/01

- Publicized

- Online ISSN
- 1745-1337

- DOI
- 10.1587/transfun.E92.A.2910

- Type of Manuscript
- PAPER

- Category
- Communication Theory and Signals

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Seokhyoung LEE, Vladimir SHIN, "Low-Complexity Fusion Estimation Algorithms for Multisensor Dynamic Systems" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 11, pp. 2910-2916, November 2009, doi: 10.1587/transfun.E92.A.2910.

Abstract: This paper focuses on fusion estimation algorithms weighted by matrices and scalars, and relationship between them is considered. We present new algorithms that address the computation of matrix weights arising from multidimensional estimation problems. The first algorithm is based on the Cholesky factorization of a cross-covariance block-matrix. This algorithm is equivalent to the standard composite fusion estimation algorithm however it is low-complexity. The second fusion algorithm is based on an approximation scheme which uses special steady-state approximation for local cross-covariances. Such approximation is useful for computing matrix weights in real-time. Subsequent analysis of the proposed fusion algorithms is presented, in which examples demonstrate the low-computational complexity of the new fusion estimation algorithms.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.2910/_p

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@ARTICLE{e92-a_11_2910,

author={Seokhyoung LEE, Vladimir SHIN, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={Low-Complexity Fusion Estimation Algorithms for Multisensor Dynamic Systems},

year={2009},

volume={E92-A},

number={11},

pages={2910-2916},

abstract={This paper focuses on fusion estimation algorithms weighted by matrices and scalars, and relationship between them is considered. We present new algorithms that address the computation of matrix weights arising from multidimensional estimation problems. The first algorithm is based on the Cholesky factorization of a cross-covariance block-matrix. This algorithm is equivalent to the standard composite fusion estimation algorithm however it is low-complexity. The second fusion algorithm is based on an approximation scheme which uses special steady-state approximation for local cross-covariances. Such approximation is useful for computing matrix weights in real-time. Subsequent analysis of the proposed fusion algorithms is presented, in which examples demonstrate the low-computational complexity of the new fusion estimation algorithms.},

keywords={},

doi={10.1587/transfun.E92.A.2910},

ISSN={1745-1337},

month={November},}

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TY - JOUR

TI - Low-Complexity Fusion Estimation Algorithms for Multisensor Dynamic Systems

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 2910

EP - 2916

AU - Seokhyoung LEE

AU - Vladimir SHIN

PY - 2009

DO - 10.1587/transfun.E92.A.2910

JO - IEICE TRANSACTIONS on Fundamentals

SN - 1745-1337

VL - E92-A

IS - 11

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - November 2009

AB - This paper focuses on fusion estimation algorithms weighted by matrices and scalars, and relationship between them is considered. We present new algorithms that address the computation of matrix weights arising from multidimensional estimation problems. The first algorithm is based on the Cholesky factorization of a cross-covariance block-matrix. This algorithm is equivalent to the standard composite fusion estimation algorithm however it is low-complexity. The second fusion algorithm is based on an approximation scheme which uses special steady-state approximation for local cross-covariances. Such approximation is useful for computing matrix weights in real-time. Subsequent analysis of the proposed fusion algorithms is presented, in which examples demonstrate the low-computational complexity of the new fusion estimation algorithms.

ER -