The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo enfoca algoritmos de estimativa de fusão ponderados por matrizes e escalares, e considera o relacionamento entre eles. Apresentamos novos algoritmos que abordam o cálculo de pesos matriciais decorrentes de problemas de estimação multidimensionais. O primeiro algoritmo é baseado na fatoração de Cholesky de uma matriz de blocos de covariância cruzada. Este algoritmo é equivalente ao algoritmo padrão de estimativa de fusão composta, porém é de baixa complexidade. O segundo algoritmo de fusão é baseado em um esquema de aproximação que utiliza aproximação especial de estado estacionário para covariâncias cruzadas locais. Tal aproximação é útil para calcular pesos matriciais em tempo real. A análise subsequente dos algoritmos de fusão propostos é apresentada, na qual exemplos demonstram a baixa complexidade computacional dos novos algoritmos de estimativa de fusão.
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.
Copiar
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
Copiar
@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},}
Copiar
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 -