Abstract
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.
Original language | English (US) |
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Pages (from-to) | 59-69 |
Number of pages | 11 |
Journal | Fuzzy Sets and Systems |
Volume | 114 |
Issue number | 1 |
DOIs | |
State | Published - Aug 16 2000 |
Keywords
- Bayes criterion
- Decision fusion
- Fuzzy sets
- Multi-sensor systems
- Total distance criterion
ASJC Scopus subject areas
- Logic
- Artificial Intelligence