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) |
---|---|
Title of host publication | IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems |
Editors | Anon |
Publisher | IEEE Computer Society |
Pages | 788-795 |
Number of pages | 8 |
State | Published - 1996 |
Event | Proceedings of the 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems - Washington, DC, USA Duration: Dec 8 1996 → Dec 11 1996 |
Other
Other | Proceedings of the 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems |
---|---|
City | Washington, DC, USA |
Period | 12/8/96 → 12/11/96 |
ASJC Scopus subject areas
- Software
- Control and Systems Engineering