TY - JOUR
T1 - A fuzzy modeling approach to decision fusion under uncertainty
AU - Samarasooriya, V. N.S.
AU - Varshney, P. K.
N1 - Funding Information:
∗ Corresponding author. Tel.: +1 315 443 4013; fax: +1 315 443 2583. E-mail address: [email protected] (P.K. Varshney). 1Research sponsored by Air Force O ce of Scienti c Research, Air Force Systems Command, USAF, Under Grant No. F49620-96-1-0185.
PY - 2000/8/16
Y1 - 2000/8/16
N2 - A multi-sensor 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.
AB - A multi-sensor 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.
KW - Bayes criterion
KW - Decision fusion
KW - Fuzzy sets
KW - Multi-sensor systems
KW - Total distance criterion
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U2 - 10.1016/S0165-0114(98)00126-2
DO - 10.1016/S0165-0114(98)00126-2
M3 - Article
AN - SCOPUS:0008798584
SN - 0165-0114
VL - 114
SP - 59
EP - 69
JO - Fuzzy Sets and Systems
JF - Fuzzy Sets and Systems
IS - 1
ER -