Extended PCR rules for dynamic frames

F. Smarandache, J. Dezert
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引用次数: 4

Abstract

In most of classical fusion problems modeled from belief functions, the frame of discernment is considered as static. This means that the set of elements in the frame and the underlying integrity constraints of the frame are fixed forever and they do not change with time. In some applications, like in target tracking for example, the use of such invariant frame is not very appropriate because it can truly change with time. So it is necessary to adapt the Proportional Conflict Redistribution fusion rules (PCR5 and PCR6) for working with dynamical frames. In this paper, we propose an extension of PCR5 and PCR6 rules for working in a frame having some non-existential integrity constraints. Such constraints on the frame can arise in tracking applications by the destruction of targets for example. We show through very simple examples how these new rules can be used for the belief revision process.
动态框架的扩展PCR规则
在大多数基于信念函数建模的经典融合问题中,识别框架被认为是静态的。这意味着框架中的元素集和框架的基础完整性约束永远是固定的,它们不随时间变化。在某些应用程序中,例如在目标跟踪中,使用这种不变帧是不太合适的,因为它确实会随着时间而变化。因此,有必要采用比例冲突重分配融合规则(PCR5和PCR6)来处理动态帧。在本文中,我们提出了PCR5和PCR6规则在具有非存在完整性约束的框架中的扩展。例如,在跟踪应用程序中,可以通过破坏目标来产生对帧的这种约束。我们通过非常简单的例子来展示这些新规则如何用于信念修正过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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