Low-Complexity Fusion Estimation Algorithms for Multisensor Dynamic Systems
Summary :
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
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
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
Copy
@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},}
Copy
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 -
English
