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Multisensor Data Fusion Based on Modified Belief Entropy in Dempster–Shafer Theory for Smart Environment
2021
IEEE Access
Multisensor data fusion is extensively used to merge data from heterogeneous sensors in a smart environment. However, sensors provide noisy and uncertain information which is a big challenge for researchers. Since uncertainty in the data is a central constraint for data fusion and decision-making systems. Dempster-Shafer's evidence theory is an appropriate method for modeling and fusing uncertain information. In this paper, a novel data fusion scheme is proposed based on the modified belief
doi:10.1109/access.2021.3063242
fatcat:v73vg7vb7jdv7ipk4evhbkx3j4