A Weighted Fusion Algorithm of Multi-sensor Based on Optimized Grouping

Author

Liyong Zhang ; Li, Dan ; Li Zhang ; Zhong, Chongquan

Author_Institution

Sch. of Electron. & Inf. Eng., Dalian Univ. of Technol.

Volume

2

fYear

0

fDate

0-0 0

Firstpage

5350

Lastpage

5353

Abstract

When measuring a certain state, multisensor can be divided into several groups, then processed by grouping weighted fusion algorithm. Based on the measurement equation of the state and the model of the noise, optimal weights of grouping fusion algorithm can be obtained by the principle of maximum likelihood estimation, and optimal grouping way of multisensor can be constructed by partheno-genetic algorithm. According to the methods mentioned above, a weighted fusion algorithm of multisensor based on optimized grouping is presented in the paper, which can achieve the optimal estimation of the state to be measured

Keywords

genetic algorithms; maximum likelihood estimation; sensor fusion; state estimation; maximum likelihood estimation; measurement equation; multisensor; optimal state estimation; optimized grouping; partheno-genetic algorithm; weighted fusion algorithm; Equations; Estimation error; Filters; Information processing; Least squares approximation; Maximum likelihood estimation; Noise measurement; Optimization methods; Sensor fusion; State estimation; Optimized grouping; maximum likelihood principle; optimal estimation; partheno-genetic algorithm; weighted fusion;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714092

Filename

1714092