信任函数组合与局部冲突处理

在证据理论框架中,数据融合是将几个来自不同证据源的信任函数组合成一个信任函数,Dempster组合规则是人们常用的方法,但由于此规则是通过按比例放大组合后焦元的基本信任指派值而使其满足信任函数的标准定义,尽管这一标准化方法有逻辑上的解释,但还是招致诸多批评,并提出了一些修正的组合规则。Dempster组合规则尤其在较强冲突情形下其组合结果是不符合常理的,因此不同证据源的冲突处理是信息融合的主要问题。该文通过分析比较已有的主要组合规则,提出了一种处理冲突的新方法--局部冲突处理法,此方法可克服已有方法的缺点,而且组合结果更加合理。

Belief Function Combination and Local Conflict Management

Within the framework of evidence theory,data fusion consists in obtaining a single belief function by the combination of several belief functions resulting from distinct information sources.The most popular rule of combination,called Dempster's rule of combination.However,combining belief functions with this operator implies normalizing the results by scaling them proportionally to the conflicting mass in order to keep some basic properties.Although this normalization seems logical,several authors have criticized it and some have proposed other solutions.In particular,Dempster's combination operator is a poor solution for the management of the conflict between the various information sources at the normalization step.Conflict management is a major problem especially during the fusion of many information sources.In this paper,we analyse and compare existing combination rules and propose a new approach to manage the conflict-Local Conflict Management.It overcomes shortcomings of Dempster's rule and other rules.