基于数据融合的Bayes估计算法研究  

The estimation algorithm research of Bayes based on data fusion

作　　者：商娟叶[1] 

机构地区：[1]西安外事学院现代教育技术中心,陕西西安710077

出　　处：《自动化与仪器仪表》2016年第2期118-120,共3页Automation & Instrumentation

基　　金：2015年校级教改项目阶段性成果(2015B25)

摘　　要：Bayes估计算法是静态环境中多传感器数据融合的常用方法,其信息描述为概率分布,为数据融合提供了一种对多源数据优化处理的手段。然而,该算法需要预先给出不同类型传感器观测对象的分布类型和先验似然概率,并要求各个假设事件之间不相容。为此,数据融合中心不得不根据这些不确定性信息进行推理,以达到目标身份识别和属性判决的目的,使得计算复杂性迅速增加。本文详细阐述了Bayes算法的基本思想,结合数据融合过程的需求,从中归纳出该算法存在的局限性,避免这些局限性影响数据融合效果。表明采用Bayes估计算法可以有效地对多源不确定性数据进行融合,并可以适应融合随时间、空间变化的数据需求。Bayes estimation algorithm is a common method for multi sensor data fusion in static environment,and it is described as a probability distribution,which provides a means to optimize the processing of multi-source data. However,the algorithm needs to give different types of sensor observations of the distribution type and the prior likelihood probability,and requires that all the hypotheses are not compatible with each other. For this reason,the data fusion center has to be based on the uncertainty information to achieve the purpose of target identification and attribute decision,which makes the computational complexity increase rapidly. In this paper,the basic idea of Bayes algorithm is described in detail. The limitation of the algorithm is summarized,and the effect of data fusion is avoided. It is indicated that the Bayes estimation algorithm can effectively fuse the multi source data fusion,and can adapt to the data needs of the fusion with time and space.

关 键 词：数据融合 Bayes估计算法 多源数据 数据处理 似然函数 

分 类 号：TP202[自动化与计算机技术—检测技术与自动化装置] O212.8[自动化与计算机技术—控制科学与工程]