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.

中文翻译：

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一种用于冲突管理的质量函数的新发散度量

Dempster-Shafer 证据理论是贝叶斯概率论的扩展，是实现多传感器数据融合的有效途径。它使用质量函数来表示不确定性，可以产生令人满意的融合结果。然而，当证据高度矛盾时，使用Dempster-Shafer证据理论融合规则将证据组合起来会产生违背常理的结果。为了解决这个问题，我们提出了一种基于 Renyi 散度 (RD) 的冲突管理新方法。然后，通过将 RD 与质量函数相结合，我们开发了 Renyi-Belief 散度 (RBD)。为了扩展其效用，我们对其进行了修改并定义了修改后的 Renyi-Belief 散度 (MRBD)。我们的方法 MRBD 整合了质量函数的特性，可以通过测量质量函数之间的差异来处理冲突。实验表明，MRBD可以有效地处理冲突。在处理了相互矛盾的证据后，我们实现了基于 Dempster-Shafer 组合规则的多传感器数据融合。此外，我们还考虑了信息质量和信念熵来增强证据的可信度。大量实例表明，该方法是可行且有效的。最后，在故障诊断的应用中，我们的方法可以有效地判断故障类型。