一种用随机集表示语言量化的新方法及其在跟踪和数据融合中的应用

Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997) Pub Date : 2002-07-08 DOI:10.1109/ICIF.2002.1020964

W. Torrez, D. Bamber, I. Goodman, H. Nguyen

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引用次数: 4

摘要

显然需要能够在数据融合中集成基于语言和基于随机的输入信息。特别是，这一需求对于解决包括网络国家入侵在内的轨道关联问题至关重要。本文通过对三种明显不同的数学工具如何组合的新见解来处理这个问题:“布尔关系事件代数”(BREA)，“模糊集的一点随机集覆盖表示”(OPRSC)和“近最优融合的复杂性降低算法”(CRANOF)。

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A new method for representing linguistic quantifications by random sets with applications to tracking and data fusion

There is an obvious need to be able to integrate both linguistic-based and stochastic-based input information in data fusion. In particular, this need is critical in addressing problems of track association, including cyber-state intrusions. This paper treats this issue through a new insight into how three apparently distinct mathematical tools can be combined: "boolean relational event algebra" (BREA), "one point random set coverage representations of fuzzy sets" (OPRSC), and "complexity-reducing algorithm for near optimal fusion" (CRANOF).

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Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)

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