Belief fusion, pignistic probabilities, and information content in fusing tracking attributes

Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509) Pub Date : 2004-04-26 DOI:10.1109/NRC.2004.1316425

John J. Sudano

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引用次数: 10

Abstract

In the design of information fusion systems, the reduction of computational complexity is a key design parameter for real-time implementations. One way to simplify the computations is to decompose the system into subsystems of noncorrelated informational components, such as a qualitative informational component, a quantitative informational component, and a complement informational component. A probability information content (PIC) variable assigns an information content value to any set of system or sub-system probability distributions. The PIC variable is the normalized entropy computed from the probability distribution. This article derives a PIC variable for a subsystem represented by the complement probabilities. This article also derives a relationship between the PIC variable of sub-system components and the system informational PIC variable. A series of pignistic probability transforms are presented that estimate the probability for any belief data set. The generalized belief fusion method of combining independent multi-source beliefs is presented.

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融合追踪属性时的信念融合、吝啬概率和信息内容

在信息融合系统的设计中，降低计算复杂度是实时实施的一个关键设计参数。简化计算的一种方法是将系统分解为非相关信息成分的子系统，如定性信息成分、定量信息成分和补充信息成分。概率信息含量（PIC）变量为任意一组系统或子系统概率分布赋予一个信息含量值。PIC 变量是根据概率分布计算出的归一化熵。本文推导了由补码概率代表的子系统的 PIC 变量。本文还推导了子系统组件的 PIC 变量与系统信息 PIC 变量之间的关系。本文提出了一系列估计任何信念数据集概率的信念概率变换。介绍了结合独立多源信念的广义信念融合方法。

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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Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)

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