Dempster-Shafer Theory and Bayesian reasoning in multisensor data fusion

被引：18
|
作者：
Braun, JJ [1 ]
机构：
[1] MIT, Lincoln Lab, Lexington, MA 02420 USA
来源：
SENSOR FUSION: ARCHITECTURES, ALGORITHMS, AND APPLICATIONS IV | 2000年 / 4051卷
关键词：
multisensor data fusion; Dempster-Shafer Theory; uncertainty reasoning; Bayesian methods; Monte Carlo simulation;
D O I：
10.1117/12.381638
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Bayesian and Dempster-Shafer Theory based methods are among the alternative algorithmic approaches to multisensor data fusion. The two approaches differ significantly and the extent of their applicability to data fusion is still being debated. This paper presents a Monte Carlo simulation approach for a comparative analysis of a Dempster-Shafer Theory based and a Bayesian multisensor data fusion in the classification task domain, including the implementation of both formalisms, and the results of the Monte Carlo experiments of this analysis.
引用
收藏
页码：255 / 266
页数：12
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