A novel divergence measure of mass function for conflict management

Dempster–Shafer evidence theory, which is an extension of Bayesian probability theory, is a useful approach to realize multisensor data fusion. It uses mass functions to represent uncertainty, which can produce a satisfactory fusion result. However, when the evidence is highly conflicting, using Dempster–Shafer evidence theory fusion rule to combine the evidence will generate the result contrary to common sense. To solve this issue, we propose a new method for conflict management based on Renyi divergence (RD). Then, by combining RD with the mass function, we develop Renyi-Belief divergence (RBD). To expand its utility, we modify it and define the modified Renyi-Belief divergence (MRBD). Our method MRBD integrates the characteristics of mass functions and can handle conflict by measuring the differences between mass functions. Experiments show that MRBD can effectively deal with conflicts. After dealing with the conflicting evidence, we realize multisensor data fusion based on the Dempster–Shafer combination rule. Moreover, we also consider the information quality and belief entropy to reinforce the credibility of evidence. A large number of examples show that the proposed method is feasible and efficient. Finally, in the application of fault diagnosis, our method can effectively determine the fault type.