Multisensor fusion with a pattern recognition approach: parametric case

Author

Reybet-degat, Ghislaine ; Dubuisson, Bernard

Author_Institution

Univ. de Technol. de Compiegne, France

Volume

2

fYear

1995

fDate

22-25 Oct 1995

Firstpage

1386

Abstract

This paper describes a fusion algorithm based on a pattern recognition approach. It is concerned with the case of a time-invariant parameter that may have multiple values when the a priori knowledge about the number of values is incomplete. With a pattern recognition approach, the sensor fusion problem is a twofold problem: first to recognize the values of the parameter and to classify the measurements with a discrimination rule and second to estimate these values taking measurements accuracy into account. Finally, the result of the sensor fusion is identified to a multimodal probability density function

Keywords

Bayes methods; decision theory; parameter estimation; pattern recognition; probability; sensor fusion; discrimination rule; measurements accuracy; multimodal probability density function; multisensor fusion; pattern recognition approach; time-invariant parameter; Additive noise; Bayesian methods; Computer aided software engineering; Counting circuits; Filtering algorithms; Kalman filters; Parameter estimation; Pattern recognition; Probability density function; Sensor fusion;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-2559-1

Type

conf

DOI

10.1109/ICSMC.1995.537966

Filename

537966