DocumentCode :
2648550
Title :
A fuzzy modeling approach to decision fusion under uncertainty
Author :
Samarasooriya, V.N.S. ; Varshney, P.K.
Author_Institution :
Dept. of Electr. & Comput. Eng., Syracuse Univ., NY, USA
fYear :
1996
fDate :
8-11 Dec 1996
Firstpage :
788
Lastpage :
795
Abstract :
A multisensor decision fusion scheme is presented in which the probabilities associated with the local sensor decisions are known to vary in a nonrandom fashion around their design values. The uncertainties associated with the local decisions are modeled by means of fuzzy sets. A Bayesian approach is used to design the optimum fusion rule for the case where the local sensor decisions are statistically independent across the sensors. In order to reach a crisp decision, the global Bayesian risk is defuzzified using a criterion for mapping fuzzy sets on to the real line. The performance of the optimum fusion rule obtained is illustrated by means of a numerical example
Keywords :
Bayes methods; decision theory; fuzzy set theory; optimisation; probability; sensor fusion; uncertain systems; fuzzy modeling approach; fuzzy sets; global Bayesian risk; multisensor decision fusion scheme; optimum fusion rule; probability; uncertainty; Capacitive sensors; Force sensors; Fuzzy sets; Information systems; Joining processes; Mathematical model; Sensor fusion; Sensor phenomena and characterization; Sensor systems; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multisensor Fusion and Integration for Intelligent Systems, 1996. IEEE/SICE/RSJ International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3700-X
Type :
conf
DOI :
10.1109/MFI.1996.572317
Filename :
572317
Link To Document :
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