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Generalized integration method of evidences with dependency information

Multisensor information fusion was defined as the integration of data and information from different sensors with the goal to produce a consistent description of the environment being sensored. Most methods make routine assumptions on the type of relation between these evidences, that is, the evidences are independent. The problem of dependent evidences has been receiving little attention in the literature. In this paper, we propose a generalized integration method of dependent evidences represented by an interval probability. A dependent parameter (DP) of uncertain evidences is first introduced. The dependent parameter DP can be represented as an interval, too. The following four types of dependency relation have been considered: minimum dependence, maximum dependence, independence, and unknown dependence. Based on the DP parameter, the algorithm to combine two evidences with dependency information is presented. The proposed method particularly well suits to computerization in the case of dependency information and obtains a satisfactory hypothesis value.