Integrating dependent sensory data

Author

Chung, Albert C S ; Shen, Helen C.

Author_Institution

Dept. of Comput. Sci., Hong Kong Univ. of Sci. & Technol., Kowloon, Hong Kong

Volume

4

fYear

1998

fDate

16-20 May 1998

Firstpage

3546

Abstract

In sensory data fusion and integration consideration, sensor independence is a common assumption. We demonstrate the impact of including dependent information in the sensory data combination process. The team consensus approach based on information entropy can improve the measurement accuracy remarkably. The major benefits of the approach are: (a) the simple linear combination of the weighted initial local estimates for each sensor; and (b) the low order bivariate likelihood functions which can be represented easily. A comparison of the team consensus approach with the Bayesian approach is presented

Keywords

CCD image sensors; Markov processes; decision theory; entropy; estimation theory; sensor fusion; sonar; Bayesian approach; dependent information; dependent sensory data integration; information entropy; low order bivariate likelihood functions; measurement accuracy; sensory data fusion; team consensus approach; weighted initial local estimates; Bayesian methods; Computer science; Estimation error; Information entropy; Measurement uncertainty; Random variables; Redundancy; Sensor fusion; Sonar; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 1998. Proceedings. 1998 IEEE International Conference on

Conference_Location

Leuven

ISSN

1050-4729

Print_ISBN

0-7803-4300-X

Type

conf

DOI

10.1109/ROBOT.1998.680994

Filename

680994