摘要
在运用Dezert-Smarandache理论(D-SmT)进行多源信息融合过程中,传感器的广义基本信度赋值(GBBA)数学模型的获取较困难,且存在主观性的问题。运用粗糙集理论,提出一种新的客观算法。通过分析传感器采集的大量客观数据之间的依赖关系,利用粗糙集理论的分类思想和协调决策等概念,按照一定规则离散化的传感器数据形成决策表,引入规则强度,计算证据的基本信度赋值,建立D-SmT广义基本信度模型,存入数据库中以备需要时查询。以P2-DX机器人为实验平台,以移动机器人本体上应用最广泛的声纳传感器为例,与声纳测量的基本特性相向对比,验证了方法的正确性和有效性。
During the multi-source information fusion with Dezert-Smarandache theory（D-SmT） ,the problem of the subjectivity exists in getting the mathematical model of general basic belief assignment （ GBBA ）. A new objective method is obtained which is integrated with rough ret （RS） theory. The dependency degree among data attributions is identified, by analyzing the massive data collected by the sensors. With the concept of classification and coordinated decision system, the data is discreted to inform decision tables. The concept of rule intensity is adopted to build GBBA model which would be stored in database to be queried when necessary. P2-DX robot is used as a platform and sonar sensors on the mobile robot are introduced as example. The correctness and validity of the developed method are verified according to sonar＇ s electrical characteristic.
出处
《传感器与微系统》
CSCD
北大核心
2009年第12期51-53,56,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(60675028)
