Multi-sensor fusion: an Evolutionary algorithm approach
Information Fusion
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Track fusion with incomplete covariance information
WSEAS TRANSACTIONS on SYSTEMS
Exact test critical values for correlation testing with application
WSEAS Transactions on Mathematics
A fuzzy-watershed image segmentation of two-dimensional gel images-survey paper
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
Ground penetrating radar slice reconstruction for embedded object in media with target follow
WSEAS Transactions on Computers
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Multi sensor fusion is an important component of applications for systems that use correlated data from multiple sensors to determine the state of a system. As the state of the system being monitored and many sensors are affected by the environmental conditions changing with time, the multi sensor fusion requires a correlation-dependent approach. The behavior of this approach should vary according to the correlation parameter. In this paper, we compare our possibilistic correlation-dependent fusion approach (PCDF) with the possiblistic combiner Dempster-Shafer. We use time-series infrared images of landmines buried in different types of soil.