An Improved Conflict Evidence Management Approach Using Base Belief Function for Uncertain Prior Information Modeling

Due to the complexity in practical environment, there are a variety of interference resulting in inaccurate information which will directly affect the final result of data fusion. In the era of big data, as the number of fusing information sources increases, even if they are consistent, conflicts may arise. However, classical Dempster combination rule in Dempster Shafer evidence theory (D-S theory) cannot solve the problem of conflict data fusion. Therefore, an improved method is proposed for conflict data fusion by assigning a base belief to each piece of evidence. In this paper, the base belief function is used to construct the initial belief degree firstly. Then, the belief entropy is calculated to get the information volume of each evidence. Dempster combination rule is used to get the final result after evidence modification, which can help solve conflict data fusion better. Numerical examples and experiments are used to verity the rationality and effectiveness of the proposed method.